Windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases.ORAL PRESENTATIONS

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The actual artificial cell division approach [46] is slightly modified to design the optimization procedure. The incorporated modifications are listed below [47] :. The hierarchical tree structure is formed throughout the generations due to the artificial cell division process. Swarms of artificial cells are considered in the optimization process to take part in the artificial cell division process.

No communication is allowed between any pair of artificial cells. Lifespan of the k th artificial cell at a certain timestamp t s is an important parameter and it is directly dependent on the fitness value f i t n e s s k as given in Eq. A huge number of swarms can significantly increase the fitness evaluations and a small number of swarms can increase time to converge and therefore, is essential to decide the swarm count moderately. In this work, the swarm count is considered is a fixed parameter.

One artificial cell can produce some new cells and the production of new cells occur at a certain distance which is inversely dependent on the fitness of the producer cell as expressed in Eq. The distance between the k th cell and any of the l th cell, which are produced from the same parent cell, must be same. Therefore, if a cell is near to global optima, then it can generate some other cells at a smaller distance and vice-versa.

Smaller steps help to search the nearest portions of the global optima cautiously so that the global optima may not be missed accidentally.

A cell does not have any effect on the population once its lifespan is over. This property helps to maintain the size of the population and prevents getting overpopulated. The successor cells of a cell can produce some other cells by the cell division process to maintain the population. The life span of a cell can belong it belongs to the near-optimal area.

The quality of a population is evaluated using the lambda function which is given in Eq. Algorithm 1 illustrates the artificial cell swarm optimization approach in brief [47]. The proposed approach adopts the type 2 fuzzy logic-based clustering approach to effectively model and handle the random uncertainties.

In most real-life applications, the uncertainty cannot be predicted in advance. A wide range of input types can produce random uncertainties. Hence, it is essential to cope up with the random uncertainties in real-life scenarios. The fuzzy C-means clustering approach is one of the widely used clustering approaches which is suitable to various problems of different domains [48] , [49] , [50] , [51].

The main reason behind the increasing popularity of fuzzy systems is the suitability of this approach in different scenarios where the crisp clustering approaches do not perform well. A single point can be a member of more than one cluster at the same time with some membership values.

The total sum of all membership values for a certain point must be one. So, the value of the membership can be anything between 0 and 1. The dissimilarity function which is optimized by the fuzzy C-means clustering approach is given in Eq.

The cluster centers can be updated using Eq. The type 2 fuzzy logic systems use separate sets of membership values that are also fuzzy in nature. This approach allows efficient modeling of dynamic input uncertainties by providing additional degrees of freedom. In this work, the type 2 fuzzy logic-based clustering approach is adopted to overcome some of the common problems of type 1 fuzzy systems like noise sensitivity, relative membership values, etc.

It is essential to improve the outcome of the segmentation process. The uncertainty of a point must be decided depending on the membership value i. So, a lower membership value indicates higher uncertainty and vice-versa. Some of the basic reason behind the adoption of type 2 fuzzy system in this work is listed below [53] :. The membership value in type 2 fuzzy systems can be calculated using Eq. The proposed approach does not require Eq. The artificial cell swarm optimization process will guide the proposed approach to determine the optimal cluster centers.

The type 2 fuzzy clustering system can be easily understood from algorithm 2 and the schematic diagram of the type 2 fuzzy system can be visualized from Fig. The ever-growing technology allows us to increase the quality of the image acquisition hardware. High-quality biomedical images can be acquired from various biomedical image acquisition devices and it is helpful in a precise analysis of the biomedical images.

Automated biomedical image analysis devices are facing some challenges due to the increasing quality of biomedical images. A high amount of spatial information creates severe problems for automated and computer-aided diagnostic systems because medical diagnostic systems demand quick and accurate results. Image segmentation plays a vital role in many automated computer-aided image analysis systems. It is essential to generate precise reports within the stipulated amount of time to provide accurate treatment to the patients.

To handle this situation effectively and to accelerate the screening process of the COVID infection, a superpixel-based novel approach is proposed in this work to segment the CT scan images.

Superpixels are useful to represent a set of pixels in a computation-friendly manner. Different approaches can be found in the literature to find the superpixel image from an input image [54] , [55] , [56]. Some superpixel computation methods like mean shift [54] and watershed [56] produce irregular superpixels and some methods like SLIC [55] generate regular superpixels. Meanshift and watershed approaches are more useful due to the capability to generate irregular superpixels. The watershed approach is simpler to implement compared to the mean-shift approach but it is sensitive to the noise which is not at all desirable for the image segmentation approaches.

In this work, the noise sensitivity of the watershed approach is removed with the help of the gradient image, which is generated using the approach, proposed in [57]. The obtained gradient image is processed using the morphological erosion and dilation-based reconstruction operations, which are given in Eqs.

Here, s e is the structuring element and it is an important parameter that controls the segmented outcome. The size of the structuring element is subjective and depends on the image under consideration. Practically, it is not possible to determine different structuring elements which are of various sizes, depending on the image. Therefore, the pointwise maximum value is computed using Eq. The number of superpixels is inversely dependent on the size of the structuring elements. It can be easily understood in Fig.

The image considered in these two figures is the T e s t 01 image [58] please refer to Table 3. Dependency of the number of superpixels on the size of the disk structuring element a — h superpixel images obtained using the disk structuring element of size 3 to 10 respectively, i Size of the structuring element vs.

Dependency of the number of superpixels on the size of the square structuring element a — h superpixel images obtained using the square structuring element of size 3 to 10 respectively, i Size of the structuring element vs. A very small lower bound is not desirable because it will produce very small regions and some essential edge information can be lost. So, the threshold value can be adjusted as per the requirement and depending on the available resources.

The conventional fuzzy C-means clustering approach often overlooks some important spatial information that can be costly in terms of the segmentation performance. Some approaches try to solve this problem by considering and blending some local spatial information in the objective function but it increases the computational cost and therefore not suitable on many occasions. Superpixels can help in this context by over-segmenting an image in many small, perceptually uniform, and homogeneous regions.

In this work, the CT images are first processed to determine the superpixels using the proposed approach and then the fuzzy artificial cell swarm optimization approach is used to determine the segmented image by finding the optimal clusters.

As discussed earlier, the type 2 fuzzy system is used to perform the segmentation. The fuzzy objective function which is given in Eq. To incorporate the advantages of the superpixel, it is necessary to modify the fuzzy objective function. The representative value is used in the objective function, and the modified objective function is given in Eq.

The cluster centers can be updated and guided by the artificial cell swarm optimization and therefore, no equation is required to compute the updated positions of the cluster center. This approach is not dependent on the selection of the initial cluster centers. The proposed procedure is given in algorithm 3 and the schematic flow diagram is given in Fig.

As discussed earlier, the properly annotated ground truth segmented images may not be available always, and therefore, some standard intrinsic cluster evaluation methods are used here to evaluate the proposed approach quantitatively. The proposed methods are applied to the images and the test results are demonstrated with the 10 CT scan images that are randomly selected which are obtained from different countries of the world.

Table 3 gives a brief overview of the test images and the test images along with their histograms are given in Fig. The experiments are performed in the MatLab Ra on a computer that is equipped with an Intel i3 processor and 4 GB main memory. The proposed method is compared with some metaheuristic optimization-based image segmentation approaches like modified genetic algorithm [67] , modified PSO [68] , improved bat algorithm [69] and modified cuckoo search method [70] in both qualitative and quantitative manner.

The visual comparison is presented in Fig. The acceptable values are highlighted in boldface. The comparisons and evaluations are performed for different numbers of clusters. A comparative study of different approaches using T e s t 01 for different number of clusters. Performance evaluation of different approaches using Davies—Bouldin index The highlighted values indicates acceptable values.

Performance evaluation of different approaches using Xie—Beni index The highlighted values indicates acceptable values. Performance evaluation of different approaches using Dunn index The highlighted values indicates acceptable values.

From the qualitative and quantitative results, it can be observed that the proposed SUFACSO approach outperforms some state-of-the-art works and can produce realistic outputs that are certainly helpful for the interpretation of the real-life CT scan images and therefore, this approach can be helpful for the early screening purposes.

At the end of each table, the average performance of the five approaches is reported which is beneficial to understand the overall performance of these methods for the different number of clusters and different cluster validity indices. In the case of average, the column-wise optimal values are highlighted instead of highlighting the row-wise optimal values.

The row-wise highlighted values talk about the performance of the individual algorithm for the different number of clusters whereas the column-wise highlighted values help to understand the performance of the individual algorithms. It can be observed that the proposed approach outperforms other approaches for most of the number of clusters as well as for most of the validity indices.

For example, on a total of 16 occasions i. These comparative results are graphically presented in Fig. In X -axis the number of clusters and in the Y -axis, the values of the corresponding validity index are plotted.

The experiments are carried out for the different numbers of clusters. A particular approach may perform well for a particular cluster count. That is why the average values of all experiments are reported at the end of each table for better interpretation. It can be observed that the proposed approach can optimize different objective functions effectively. Actually, the experiments are carried out on CT images in the first phase and CT images in the second phase. It is already mentioned in Section 5.

Results that are obtained from all images are not possible to report in this stipulated amount of space. Therefore, only some results that are obtained from some selected images are reported. Apart from these tests, the proposed approach is also compared with some of the active contour models based on some standard parameters like accuracy, precision, and recall. This comparison is performed by using the database that is available at [71].

This dataset is created by collecting sample images from 49 patients with age range 32—86 years. The obtained average results are reported in Table 8. The rate of convergence is an important parameter to be studied. The performance evaluation remains incomplete without studying and comparing the convergence of different algorithms.

The convergence analysis gives a clear view of the comparative performance of different algorithms for the different numbers of clusters. The graphical analysis of the convergence is presented in this subsection using the image T e s t 01 for the Dunn index. In Fig. In a single plot, four separate curves are indicating four different clusters. These curves show that the proposed approach can efficiently segment the images for a higher number of clusters.

Moreover, the proposed approach also outperforms some other methods in terms of convergence besides quantitative and qualitative performance. The time complexity is an important aspect that is to be analyzed. From the detailed discussion of the proposed approach, it can be noticed that the proposed approach can be viewed as a two-phase procedure where the watershed-based computation approach is used to determine the superpixel image from the underlying image in the first phase and the optimal segmented outcome is computed in the second phase.

The task of optimization is performed using the proposed fuzzy ACSO approach. The gradient information of an image is used to avoid the noise sensitivity of the water-shed based superpixel computation process. The watershed-based technique is a simple method to compute the superpixel and the implementation follows linear complexity [56].

It is quite inspiring and lucrative to adopt this approach on different occasions. In the optimization part, the fuzzy objective function is optimized by using the proposed fuzzy ACSO method. The ACSO approach is an effective and efficient approach that can be executed in linear time [47]. So, the proposed approach is efficient enough and can be effectively used in various real-life problem-solving scenarios. This approach can effectively process high-quality images with the help of the proposed superpixel-based approach that is an essential quality for the real-life application of an image segmentation approach.

This approach removes the dependency of choice of the initial cluster centers as well as the ACSO approach determines the optimal cluster centers by optimizing some validity indices. These advantages motivate us to apply the proposed approach to automatically segment the radiological images that will be certainly helpful in diagnosing some symptoms of COVID The experimental outcomes show the efficiency of the proposed approach.

Under this pandemic environment, this work is designed hoping that it can help physicians and other domain experts to some extent in the early diagnosis of the disease. Early diagnosis can prevent the drastic spread of this highly infectious virus. Quantitative results do not have any direct implications in real-life diagnosis. The segmented outcomes are useful in the diagnosis process.

Physicians can investigate the segmented outcomes to find some prominent and common features as mentioned in Table 2. The segmented images will be helpful in the easy interpretation of the radiological images. The proposed SUFACSO approach is an efficient image segmentation approach that can effectively segment the radiological images that highly useful in the easy interpretation of these images. There is a high possibility that a suspected patient can spread the disease in the community completely unwillingly.

The proposed approach can reduce this chance because an initial screening can be performed by the physicians comfortable with the help of the proposed SUFACSO approach. It is worth mentioning here that the proposed approach is neither a replacement of the RT-PCR test nor it can confirm the presence of the virus accurately.

However, this approach can be helpful in an initial screening at an early stage that will restrict the spread of this highly infectious virus by separating suspected patients from the rest of the community. The obtained results indicate that the proposed approach is suitable for real-life scenarios and also performs efficiently. This approach can be easily adapted for the automated screening purposes of the COVID infected patients. It is assumed the quality of the CT scan images is considerably high and the performance of the proposed approach is not verified against the presence of noise.

It will be interesting to study the proposed approach in the presence of noise. The scalability of the proposed approach to different types of biomedical images can be explored in future studies. Missing manual annotations can jeopardize the generalizability of the proposed work.

On the other hand, the obtained results are quite promising and encouraging. From the best of the knowledge of the authors, there is no publicly available manually annotated dataset for the chest CT scan images of the COVID positive cases. Although the proposed approach is efficient enough to segment the CT scan images automatically and produces realistic segmented outcomes still, some important drawbacks can be observed in this proposed approach that can be addressed in the subsequent works.

One important drawback of the proposed approach is that it cannot automatically determine the number of clusters and it can be overcome in future works. Automated estimation of the clusters can make this approach more realistic, robust, and application friendly.

The proposed method can handle only a single objective at a time. Therefore, the proposed approach is not suitable for multi-objective optimization issues unless enhanced further. The number of images in the dataset is not very large. So, the proposed approach can also be tested on some additional CT images of COVID infection as well as on some standard dataset of the biomedical images.

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Machine learning approaches which link material with outcome can enhance impact of pharmacovigilance methods and tools. In order to utilize the full potential of such options it is critical that the regulatory framework is updated to enable such approaches which complement traditional PV and can drive efficiencies and higher effectiveness in the risk communication process.

Collaboration within the network of industry and regulators is essential to further such research and maximize the impact on value for patients, HCPs and sponsors. Introduction: Large amounts of data associated with safety issues are generated along the entire lifetime of drugs, from its infancy as preclinical leads, through its adolescence as clinical candidates, all the way up to its adulthood as marketed drugs exposed to the human population. Across the different stages in the life of a drug, some of the data collected initially may be confirmed and consolidated with data at an advanced stage, whereas other data may not be translated, and in some cases may even contradict, those safety signals that are ultimately observed in the human population.

Collecting and properly integrating such an heterogenous pool of data is a complex and tedious task. But even if one manages to put all data together, the construction of useful models to anticipate and detect drug safety signals remains a challenge. Objective: The presentation will cover our efforts to connect data from in vitro safety pharmacology, preclinical toxicology, clinical safety and post-marketing spontaneous reports for over 9, small molecule drugs, combination drugs, and biologics.

A novel consensus approach using various statistical and machine learning methods to anticipate side effects of potential safety concern, detect adverse drug reaction signals and perform pharmacovigilance analyses will be introduced. Use case application examples to individual drugs and drug classes will be discussed.

Methods: Our consensus approach to post-marketing surveillance integrates four different methodologies based on detection of prior safety markers, identification of class reactions, statistical projection of disproportionalities based on reporting frequencies and velocities, and machine learning models of translational safety data.

Results: Results on the validation of our approach to anticipating adverse drug reactions of safety concern to the population at the postmarketing stage based on i in vitro safety pharmacology data, ii preclinical toxicology data, iii clinical safety data and iv the first sample of 25 postmarketing spontaneous reports will be presented.

Based on data available in each case, the performance of the different methods varies for different drugs, drug classes, and side effects. A discussion on performances in selected use cases will be included. As an example, the analysis of long-term PARP inhibition on circadian patterns and its dependence on the reporting bias by consumers will be discussed. Conclusion: Integration and modelling of the large amount of translational safety data currently available from all phases of drug discovery, development and post-marketing to anticipate and follow adverse drug reactions opens an avenue to a whole new perspective in pharmacovigilance.

Introduction: Psychedelics are unique psychoactive chemicals that can change consciousness by acting on 5-HT2A receptors []. There is limited knowledge concerning the online interest in psychedelics that we can extrapolate via trends websites. Objective: We aim to examine the online information-seeking behavior concerning the most popular psychedelics, including cannabis—a quasi-psychedelic—in the European Union EU members of interest and the UK before and during the pandemic.

Methods: We designed a “dictionary” of terms to extract online search data from Google Trends concerning psychedelics and cannabis from Jan to 1-Jan We conducted a triple Holt-Winters exponential smoothing—additive model—for time series analysis to infer seasonality [4, 5].

We utilized hierarchical clustering—an unsupervised machine learning method—to explore clusters of countries concerning the spatial geographic mapping of these chemicals.

We also implemented—a t-test—for comparing the slope difference of two trends before versus during the pandemic. Results: There was an evident seasonal pattern for cannabis, NBOMe, and psilocybin in almost all nations of interest. Similar patterns existed in France and the UK, while those in Germany, Sweden, and Romania had relatively shorter periodicity. Analysis of slopes and hierarchical clustering conveyed differentiated patterns concerning the temporal and spatial mapping, respectively, while contrasting the two periods before versus during the pandemic.

Conclusion: Cannabis and psychedelics follow somewhat a consistent pattern concerning seasonality across Europe; some correlate with the seasonal harvesting of mushrooms, and others with public holidays, including Christmas, the new year holiday, or school breaks.

The pandemic influenced some significant changes concerning the online interest in the EU and the UK; nonetheless, we should rely on more rigorous longitudinal and experimental study designs—possessing a superior level of evidence—to confirm the causal relationship. However, these patterns might be insightful for decision-makers and regulatory authorities—like the EMCDDA—to prognosticate and prevent addiction catastrophes.

Understanding and using time series analyses in addiction research. Carhart-Harris RL. How do psychedelics work?. Current Opinion in Psychiatry.

Novel psychoactive substances: types, mechanisms of action, and effects. British Medical Journal. Robust forecasting with exponential and Holt—Winters smoothing. Journal of Forecasting. Gardner Jr ES. Exponential smoothing: The state of the art—Part II. International Journal of Forecasting. Introduction: Continuous monitoring of the safety profile of drugs is one of the critical processes of pharmacovigilance. As medical literature might be valuable source of safety data, especially for rare, unlisted, serious cases, all MAHs are obliged to medical literature monitoring MLM in all marketing countries [1].

This approach can be changed through modern automation techniques. Objective: To develop and test a tool for automated monitoring of local literature and enhance drug safety data identification. Methods: Modern programming approaches were used to create PV platform, intended for automated literature screening. GAMP 5 recommendations were used to prove the validation status. Results: We developed a tool—DrugCard PV platform which screens local medical sources for updates on a weekly basis.

Till May we added around local journals originated from 10 countries that cover different therapeutic areas. Our tool automatically searches for defined keywords drug trade names, active substances in published articles. Different file formats can be screened including text, pdfs, images etc. In case a new issue of a journal is published—a PV specialist will receive an email notification.

The mandatory features of a validated computerized system, like audit trail, logs, reports are also present here.

Instead of manual reading of the whole journal issue the user only should read a separate article, analyze whether there is a valuable safety data and label it depending on the content.

PV specialists may work together inside the platform and provide a quality check for labeled articles. Our pilot study of how a new tool may improve the efficiency revealed interesting results. Despite the dramatically decreased amount of time needed, the number of identified ICRSs from literature increased.

During the abovementioned pilot study of automated local literature monitoring lasting 2 months, 31 safety cases were identified valid and non-valid ICSRs. This is much more than usual rate of safety cases finding. It offers increasing efficiency in safety information identification with less time spent on routine activities. Certificate of copyright in Ukraine. Hyperacute toxicity is a recent newly described entity, albeit incompletely characterized [3]. We selected reports with available information to calculate a plausible time-to-onset.

Events of interest were classified into fulminant within 7 days and hyper-acute cases within 21 days, i. Cases were described in terms of demographic and clinical features: age, gender, anticancer regimen combination vs monotherapy , therapeutic indications, seriousness hospitalization , case fatality rate CFR, namely the proportion of cases where death was reported as outcome , co-reported symptoms, co-reported irAEs.

The Immune-Adversome was estimated considering events as nodes and co-reporting as links. Hyperacute cases 18, represented Monotherapy was reported in the majority of cases Pyrexia, diarrhea, fatigue, dyspnea were the most frequently reported symptoms.

Hyperacute myocarditis was reported in Among fulminant cases, most frequent irAEs were interstitial lung disease , colitis , hypothyroidism , and myocarditis Other co-occurring irAEs were colitis-hepatitis-thyroiditis, and arthritis and psoriasis. Our network approach may complement traditional disproportionality analyses in pharmacovigilance for a more effective signal detection technique, thus supporting regulatory and clinical monitoring, especially in complex scenario such as oncology. Target Oncol ; — Oncologist ; — Hyperacute toxicity with combination ipilimumab and anti-PD1 immunotherapy.

Eur J Cancer ; — Introduction: The prolongation of the QT interval is a serious and potentially fatal adverse reaction that has led to the discontinuation of many drugs including some opioids.

Data mining on pharmacovigilance databases can detect signals that identify early the risk associated with some drugs. Results: A total of drug-reaction pairs was found in opioid reports. Analysis of individual opioids show significant signals for QT prolongation for each drug. The temporal evolution of the different signals according to the number of reports included from to shows early significant positivization of signals in the first 6 to 12 months.

Underlying mechanism is unknown, but it seems to be linked to hERG channel blocking. We propose the evaluation of the trend of change in the confidence intervals of the disproportionality parameters as a measure that can predict the occurrence of clinical events at the population level and a posible usefull strategy to minimize adverse reactions.

Introduction: Language and speech are increasingly debated as potential markers for diagnosing and monitoring patients with affective and psychotic disorders 1—3.

However, many neglected factors may confound communicative atypicalities. A comprehensive list of potential confounding drugs will support the design of robust communicative marker studies.

Objective: We aim at identifying a list of drugs potentially associated with speech and language disorders, within psychotic and affective disorders. Within the FAERS, we considered separately 3 populations psychotic, affective and non-neuropsychiatric disorders , to account for the confounding role of different underlying conditions. Robustness analyses were performed to account for the biases.

Results: We identified a list of potential expected and 91 unexpected confounding medications for the identification of communication markers of affective and psychotic disorders e. We developed also a MedDRA query proposal for speech and language conditions, formalization of possible biases, and related analyses to account for them.

Conclusion: We provided a list of medications to be accounted for in future studies of communicative bio-behavioral markers in affective and psychotic disorders. The methodological procedure we developed does not simply facilitate future investigations of communicative biomarkers in other conditions, more crucially it provides a case-study in more rigorous procedures for digital phenotyping in general.

Insel TR. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology. Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis. Schizophr Res.

Introduction: The comparison of safety profiles for products recently on the market is difficult. There is a lack of methodology for quantifying the potential differences between products that have the same indication. Objective: Provide the tools to quantify the differences in spontaneous reporting between two products. An Euclidian distance from the EBGM to the diagonal line measures the deviation from what would have been expected under the null assumption of similar safety profiles.

As the deviation does not capture the statistical uncertainty around the estimate, we propose as measure of the deviation the minimal distance of the four Euclidian distances calculated from each of the credibility intervals around the EBGM post Product A and Product B.

A visualization capturing the global trend of the most substantial differences in reporting was generated. Conclusion: This relatively simple method can provide quantification of the differences in reporting and could help prioritize one product over the other for some population subgroups. Introduction: The application of text mining approaches to identify adverse events AEs from electronic health records EHRs is a growing area of interest in pharmacovigilance research.

In veterinary medicine, the majority of EHRs consist of unstructured clinical narratives, hence the development of appropriate methods for identifying AEs of interest is an important step in the research process. Identifying renal disease poses a specific challenge as the event may be described in narrative form or implied by reported test results or the use of renal disease specific medications.

In this study we developed regular expressions regexes to identify relevant mentions of renal disease in veterinary free text clinical narratives. Objective: To develop a method for identifying veterinary patients with renal disease in free text clinical narratives. Methods: Using VeDDRA terminology as a starting point, we used an iterative approach to develop a series of regexes which were then applied to a random sample of 10, clinical narratives.

In order to measure precision, clinical narratives containing a match to the regexes were reviewed against a case definition by two independent reviewers and disagreement was settled by consensus. Terms in the final regex were derived from three sources—VeDDRA, a word embedding model and expert opinion. To determine recall, the final regex was applied to a sample of consults where the main presenting complaint was deemed to be renal disease by a veterinary clinician.

Expanding this terminology using a word embedding model improved the PPV to 0. Following changes suggested by a veterinary expert, the PPV of the final regex was improved to 0. When the regex was divided into three components, the PPV for these individual portions was mentions of renal disease 0. When compared against the veterinary clinician validated sample of renal disease consults recall was 0.

Conclusion: The developed regex can be used to identify animals with renal disease, with mentions of renal disease treatment being the most specific indicator of clinical disease. This method can be employed to filter potential cases of interest from large datasets for use in observational studies.

Introduction: We use AI in our everyday lives probably without even realising it. There are many discussions about the use of AI in PV and the potential innovation that it could bring but given the conservative nature of our business and having to work in a highly regulated environment, how can we build confidence to get us over that barrier.

Will having the regulators use the same AI make us more comfortable or will legislation be necessary to drive us forward? Objective: Explore why PV has lagged behind with AI technology that is commonplace in other parts of our lives and business. Aspects of AI, such as machine learning, are used in areas such as early disease prediction, clinical diagnosis, outcome prediction and prognosis evaluation, personalized treatments, drug discovery, manufacturing, clinical trial research, and more.

In our personal lives, services like Amazon and Google use AI to understand and target their customers and we accept that as normal.

The objective of this presentation is to explore the reluctance of accepting AI in PV and how we can move towards overcoming those obstacles. We will look at some real-life practical examples where AI in PV has worked and what it took to get there. Conclusion: We will show that the practical application of AI is achievable and has been achieved in the high volume environment of a regulatory authority.

Many of the AI features used by the RA, and the lessons learned from that project, can also be applied in industry, so why are we waiting? Introduction: Access to case narratives during signal assessment is crucial to provide a more complete picture of the cases [1], however patient confidentiality needs to be considered.

Sharing of narratives while preserving privacy requires de-identification—the removal or replacement of personal identifiers. Automating this task can help with increasing data load.

To ensure patient confidentiality throughout the full pharmacovigilance process, the narratives should be de-identified early in the process. Person names—one of the more common identifiers in case narratives—can lead to in- direct identification of patients but are challenging to recognise in free text. Objective: To develop and evaluate a method for automated de-identification of names in case narratives.

Methods: We use an ensemble of BERT [2]—a state-of-the-art language model using deep-neural network—combined with hand-engineered rules for detecting names. Our model is trained on i2b2 deidentification challenge data [3] combined with unprocessed data from the Yellow Card system[4] provided by the MHRA. Because names are rare in the Yellow Card data, the training dataset is prepared using active learning through an independent model.

Model testing is done on a separate, manually annotated dataset. Evaluation of the deidentification is guided by: 1 how often clinically relevant information is removed and 2 how identifiable the narratives that the model fails to completely de-identify are. We define three categories of identifiability: a Directly identifiable, where subject identification is very likely with the leaked information e.

Results: Out of the 71 narratives with names and initials, only 12 contained occurrences missed by the system. Manual evaluation found only one directly and one indirectly identifiable narrative due to leaks. It should be noted that the leaked direct identifier was a foreign, non-English name.

A single narrative may contain multiple occurrences of names, the table presents results per occurrence. Conclusion: Automated de-identification of names is possible without compromising clinically relevant information. Our method can recognise and mask a vast majority of names and most initials while leaving most of the information untouched.

Qualitative evaluation shows that the rare leaks that occur tend not to make cases identifiable. Clinical stories are necessary for drug safety. Clin Med. J Biomed Inform. Medicines and Healthcare products Regulatory Agency. The Yellow Card scheme: guidance for healthcare professionals, patients and the public [Internet]. Introduction: Metronidazole is a nitroimidazole antibacterial drug that is mostly used to treat anaerobic bacteria and protozoa infections.

The adverse side effects of metronidazole include gastrointestinal upset, metallic taste, urticaria, headache, peripheral neuropathy. Metronidazole-induced pancreatitis has been rarely described in the literature so far.

Objective: We report a rare case of an acute pancreatitis associated with metronidazole which occurred as a result of a prescription error. Methods: This case was reported in February to The National Centre of Pharmacovigilance and evaluated according to the updated French method of causality assessment. Results: A year-old male patient with a past medical history of chronic viral hepatitis B treated with entecavir since , presented to the surgery department with an acute onset of a severe epigastric pain radiating through to the back associated with hepatic colic with nausea and vomiting.

On exam, he had severe epigastric tenderness. Relative negatives in the history included, no lithiasis, no known drug allergies, and no alcohol consumption. Patient symptoms and lipase improved within 3 days after metronidazole withdrawl and initiation of supportive care. Conclusion: The likelihood of metronidazole as the incriminating agent was likely in front of a suggestive delay and favorable outcome after the drug withdrawl. It was suggested a the possible dose-response mechanism between metronidazole use and occurrence of pancreatitis, and this case draw attention to the possible acute pancreatitis associated with metronidazole due to a prescription error.

Metronidazole-associated pancreatitis. Introduction: The possibilities of using current scientific principles to create tools to help give efficiency and help to nurses thereby reducing stress and the potential for errors. Also enable patients to maintain independence and less outside contact as technology is used to expand the reach of telehealth.

Solutions will be adaptable for independent use by the sight, hearing and mentally challenged. The 1st hurdle is to make it easier for patients and staff to accomplish what they have to do safely and consistently. Objective: To simplify the taking of all drugs and supplements using IoT technology. This a paradigm shift from the many efforts to mitigate the challenges of the many aspects of drug delivery. Here medication is always kept in the labelled, legal safety of the original dispensed container until consumed.

Safety concerns of pre-pouring will no longer exist. Authentic real-tine medication usage data will be available. ISoP and other safety management organizations will be able to execute many tasks with precision. Methods: The innovation is a multi-compartment device that holds a medication container in each compartment.

The device has a display that resides in the lid or may be at the front of a drawer type or wall mounted unit. The concept of assigned location forms the basis for these innovations.

Stored instructions for many aspects of care and follow-up resides in the device and will be communicated via the display appropriately. It can be connected to a larger display, cellphone or other mobile device. Medicine containers are scanned to capture dosing instructions. The assigned location lights up. The container is placed within the compartment and receives an alert at dosing times.

The compartment stays lit until the nurse picks up and replaces the container. Video may be activated. Biometric access ensures identity and pill count and time are automatically recorded. Results: Feasibility indicates that the must touch to silent feature is a powerful feature that aids adherence. Also the timing methods that ensures safe dosing separation helps to ensure all doses are taken in a given day even if late taking a dose.

Relative time rather than time of day dosing is used. Conclusion: Believed to be unsolvable, these discoveries will open the door to the science of individual ingestion by effortlessly notifying and guiding individuals in the consumption and effects of medicines and other items for a safer and healthier life experience.

Powerful data will be generated for use by ISoP. Introduction: The Summary of Product Characteristics for Ceftriaxone states that as with all beta-lactam antibacterial agents, serious and occasionally fatal hypersensitivity reactions have been reported [1]. However, the frequency is stated as unknown. Out of 46 reports to Ceftriaxone in the Uganda ADR database, 7 of these are of anaphylactic reactions, and one of them was fatal for the paediatric patient.

It is not clear in cases of injurious or fatal drug effects who should bear the liability. Objective: To present a case study of a successful legal resolution of a fatal medication error to Ceftriaxone with the involvement of the regulator. Methods: This is a retrospective case report. Results: A one-and-a-half-year-old male child was diagnosed with septicaemia with diarrhoea and admitted to a hospital.

Day one treatment with Ceftriaxone was stopped due to a reaction of difficulty in breathing. A switch to Ciprofloxacin happened and the patient began to improve. Due to a weekend staff shift change, the change to ciprofloxacin was not noted resulting in re-administration of ceftriaxone and anaphylaxis that caused the death of the patient despite all efforts to resuscitate.

National Drug Authority performed a causality assessment of the serious adverse event and found that administration of Ceftriaxone was related to the outcome of death. However, it was noted that this was a medication error with no malice aforethought and therefore the health care provider was not liable. Publication of these results can aid in encouraging reporting rates among patients and providers. Ceftriaxone 1g Powder for solution for injection.

Accessed March 9, General characteristics, economic burden, causative drugs and medical errors associated with medical damage litigation involving severe cutaneous adverse drug reactions in China. Journal of Clinical Pharmacy and Therapeutics. Liability associated with prescribing medications. Primary care companion to the Journal of clinical psychiatry. Bhatt AD. Drug-related problems and adverse drug events: negligence, litigation and prevention.

The Journal of the Association of Physicians of India. PMID: Physicians’ liability for adverse drug reactions. Southern Medical Journal. Introduction: Pharmacotherapeutic Follow-up is a professional practice focused on identification, prevention and resolution of Drug-Related Problems and the causes or errors that originate these problems in patients [1—4]. Methods: Clinical pharmacists perform Pharmacotherapeutic Follow-up of hospitalized patients through three evaluations: drug reconciliation, pharmacotherapeutic profile and drug prescription suitability, identifying Drug-Related Problems and medication errors and recording their activities in two databases: the first consists of the evaluation that is carried out weekly and the second corresponds to the pharmaceutical interventions; these bases are validated monthly with each other.

For this study, data is taken from both databases in the period from June to December and the risk that was reduced by accepted pharmaceutical interventions is calculated.

Results: In the review of the database of activities carried out, a progressive increase in the number of evaluations done by clinical pharmacists was observed in drug reconciliation, pharmacotherapeutic profile and drug prescription suitability, identifying discrepancies, medication errors and Problems Related to Medications.

In the database of pharmaceutical interventions, a significant increase in the risk that was reduced associated with the interventions carried out and accepted was observed; as it can be identified in figure 1, which ranges from Conclusion: The pharmaceutical interventions allowed to improve the prescriptions and with it, identify Problems Related to Medications and medication errors before causing harm to the patient, making the drugs safer.

Farmacia Hospitalaria, 37 1 , 59— Introduction: Some side effects of anticholinergic drugs can be relatively harmless such as dry mouth or constipation, but in some cases, they can manifest themselves in the form of heart arrhythmias or as worsening of dementia or delirium.

The elderly are more prone to show anticholinergic effects, due to a progressive decrease in acetylcholine levels, and are often also treated with drug polytherapy with additive effects which leads to an anticholinergic cognitive burden ACB [1—4]. Objective: Verify whether it is possible to identify patients who may experience an adverse reaction due to ACB in real clinical practice through a pharmacological investigation, identify which drugs are the possible cause and re-evaluate the therapy to prevent the onset of adverse reactions.

Clinical analysis was performed by assigning a score of 1 to each adverse event attributable to ACB in the central nervous system, mouth, eyes, heart, gastrointestinal tract, bladder, and skin. Results: In 34 patients, the total number of drugs prescribed was with an average of 8. In these patients, the major drugs responsible for elevated ACB were quetiapine, chlorpromazine, and paroxetine, all three with a value of 3. The 5 patients also showed clinical signs of ACB.

Conclusion: Computerized determination of CBA was helpful in preventing adverse reactions, identifying which drugs are responsible for adverse reactions and modifying therapy to avoid the occurrence of adverse events. Drug therapy analysis is useful in conjunction with clinical evaluation and can be a valuable tool used in conjunction with tools such as Mini Mental Status. A preliminary study of anticholinergic burden and relationship to a quality of life indicator, engagement in activities, in nursing home residents with dementia.

J Am Med Dir Assoc. Epub Jan 9. Epub Jun J Am Geriatr Soc. Epub Aug J Nutr Health Aging. Introduction: Presence of a strong medication safety system can prevent many potential medication errors MEs by enforcing safety monitoring on the ordering, prescription, preparation, and administration of medicines [1].

Furthermore, a well established medication safety system can solve many causes of communication problems which account for over half of all causes associated with medication errors through its electronic based system. Unfortunately, many of the existing electronic health records EHRs were designed for purposes of medical billing rather than for medical care, resulting in challenges for using the recorded data for safety data capturing.

Moreover, commercially available electronic prescribing and computerized physician order entry systems are cost-prohibitive for many health organization, especially non-profit ones. In a previous research project, Egypt Chapter of International Society of Pharmacovigilance ISoP was engaged in developing such system in the hospital of Palestine Red Crescent Society PRCS in Cairo to support identifying MEs that were experienced by refugees through remodeling and adding new features to the existing hospital management system.

Objective: The objective of this study was to assess the effectiveness of introducing internally low-cost electronic prescription system in reducing the frequency of MEs of different types. Methods: A pre- and post-intervention study was conducted to compare the frequency of MEs before and after replacing the traditionally used paper-based system with an internal electronic-based system in hospital setting.

MEs were collected by reviewing randomized medical records at base line and after one year of introducing this electronic-based system. More focus was given to medical records of elderly patients and emergency ward. The prescribing errors, transcribing errors, dispensing errors, administration errors were investigated.

Results: We analyzed paper-based prescriptions at baseline and paper-based and electronic prescriptions at one year of follow-up. The errors were Conclusion: The adoption of internal electronic prescription systems was effective in markedly reducing the frequency of MEs compared to the paper-based system in a low-resource setting where the expense on complex commercial electronic solutions are burden for institutions.

Elhawary, M. Drug Saf 45, 97—99 Introduction: In spite of its large use, a conspicuous number of paracetamol adverse reaction reports have been recently collected, due to overdosage or posologic mistakes.

A recent metanalysis by BMC Med Inform Decis Mak [1] has inserted paracetamol in the list of the six drugs causing severe ototoxicity and a pharmacovigilance retrospective study [2] has highlighted that it induced 1. Another recent review on the analgesic standard doses of paracetamol has demonstrated its grade of toxicity, at the maximum prescribed dose [3].

Methods: A survey of 7 questions on standard dosage, dose adjustment and antidotes to paracetamol overdose was submitted to 36 health professionals nurses, pharmacists, oncologists, hematologists, surgeons in the Cancer Institute of Bari.

The answers were collected and charted in diagrams, in order to soon identify critical evidences. Conclusion: The collecting data have demonstrated the clinical need to manage accurately old and apparently well-known drugs to grant a controlled clinical risk in hospitals.

Pharmacovigilance is a duty for health professionals and the awareness that also old drugs can be causes of toxicity is a substantial starting point for safety of care. Hyunah Shin, Suehyun Lee. Saudi Pharm. Paracetamol: not as safe as we thought? A systematic literature review of observational studies. Ann Rheum Dis Mar;75 3 —9. Paracetamol: mechanism of action, applications and safety concern.

Acta Pol Pharm. Jan—Feb ;71 1 — Eur J Pain. Introduction: High interest in the last two years was globally put by Health Authorities on the recording, coding, and reporting of medication errors to ensure the safety and effectiveness of the use of medicines and to provide reliable information to healthcare professionals and patients.

Medical coding is a prerequisite for efficient, effective, and reproducible data outputs. Methods: A sample of medication error coding results was assessed for accuracy and consistency of MedDRA coding and identification of main types of coding errors. Results: One-third of the records could not be assessed due to incomplete or unclear verbatims. In one-third, code assignments were correct, but another third of the sample was not adequately coded.

Most frequent coding errors corresponded to vague PT assignments, while more detailed information was available for a more precise coding. This observation is similar to the EudraVigilance database, where some of the most assigned MedDRA terms for medication errors also represent vague concepts.

Conclusion: These findings indicate that understanding of medication error documentation and assessment and of MedDRA content and coding guidelines need to be reinforced. Introduction: Pediatric intoxications represent one of the most common causes of harm to children under the age of six and the fourth leading cause of death in developing countries [1—2].

Data collection and systematic analysis of intoxication cases is of fundamental importance to gain a greater knowledge of toxic domestic, environmental and pharmacological agents [4—5]. Gaslini for the period from January to December All poisoning were retrieved from the Hospital Central Database using the International Classification of Disease ICD 9 classification code system, and subsequently entered into a local database for data management.

Descriptive statistics were undertaken. Our analysis included therefore poisoned patient cases, 70 were from females and 74 from males, with a median age of 3 years old. Out of the total of accesses, Poisoning severity and the need for hospitalization have also been investigated.

Conclusion: Implementation of high-performance data collection systems in the Emergency Department could be decisive in guiding clinical choices.

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– Windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases

 
The proposed approach adopts the type 2 fuzzy logic-based clustering approach to effectively model and handle the random uncertainties. Retrieved February 17, Our findings on long term risk-benefit profile of immunosuppressive therapy may be helpful to define the optimal drug therapy in kidney recipients. Due to the large number of spontaneous reports associated to covid vaccines, highly significant TTO signals could be detected whereas there are no clinically relevant unexpected temporal patterns. Retrieved August 5, Drug-related admissions and hospital-acquired adverse drug events in Germany: a longitudinal analysis from to of ICDcoded routine data. Only through LTSC in-place upgrades.

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Windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases.1. Introduction

 

The new PMC design is here! Learn more about navigating our updated article layout. The PMC legacy view will also be windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases for a limited time. Federal government websites often end in. The site is secure. Due to the absence of any specialized drugs, the novel coronavirus windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases or COVID весьма problem with onedrive windows 10 слова one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID patients.

This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach uses a novel superpixel computation method which is helpful to effectively represent the pixel intensity information which is beneficial for the на этой странице process.

Superpixels are further clustered using the proposed fuzzy artificial cell swarm optimization approach. So, a twofold contribution can be observed in this work which is helpful to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be isolated at an early phase to combat the spread of the COVID virus and it is the major clinical impact of this work. Both qualitative and quantitative experimental results show the effectiveness of the proposed approach and also establish it as an effective computer-aided tool to fight against the COVID virus.

On average, the proposed approach achieves 1. The general direction of this research is worthwhile pursuing leading, eventually, to a contribution to the community. Automated computer-aided systems prove their effectiveness and real-life applicability in various scenarios. Automated systems have a diverse domain of applications and sometimes, these systems are inevitable to perform certain jobs efficiently and in a cost-effective and highly time-bound manner.

This domain is evolving day-by-day and continuous effort can be observed from various researchers to enhance this domain. Computer-assisted systems can be categorized in two ways. The first one is the supervised approach in which some properly annotated data are required to perform the classification and interpretation job [1][2].

Therefore, these automated systems are dependent on the ground truth data typically produced by some domain experts. But, it may not be always possible to acquire the properly annotated ground truth data due to the involvement of human experts [3]. Sometimes, some cases are not well-defined or not seen earlier, and therefore, it is very difficult to get some ground truth data for those cases.

Unsupervised systems can be helpful in this context because these systems are not dependent on the ground truth data and can automatically explore some patterns from the underlying dataset by utilizing the surrounding knowledge [4][5][6][7]. So, the unsupervised approaches are helpful in those situations where a sufficient amount of properly annotated ground truth data are not available.

The unsupervised computer-aided systems are widely applied in different domains of research [8][9]. Biomedical image analysis is no exception and exploits the advantages of unsupervised automated systems in various phases. Radiology is one of the important and frequently used parts of the biomedical imaging domain which is serving as an important tool for noninvasive diagnostic systems.

X-ray, CT Scan, etc. Automated systems are helpful to analyze and diagnose different patients automatically and automated radiological image analysis systems are also helpful in preparing precise and timely reports by reducing the human intervention and also reducing some unintentional human-made errors. Physicians, radiological technicians, and all other concerned domain experts can be significantly benefitted from the advancement in the field of computer-aided radiological image analysis systems.

Apart from the automated analysis of the radiological images, computer-assisted systems can windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases helpful in windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases tuning of the image acquisition hardware, image preprocessing, quality control, selecting the appropriate level of radiation, and many more.

Therefore, automated systems can act as a helping hand in the decision-making process. In Table 1 some of the related biomedical image segmentation works of literature are discussed which is helpful soft aim download free a better understanding of the current trend and status of the same.

Apart from these works, some comprehensive studies can be found in [13][14][15][16][17]. Apart from these works, some of the most recent and relevant works can be found in [28][29][30][31][32] that can be referred to, to understand the further advancements of this domain.

In this context, it is worth mentioning here that the active contour model is an effective way of image segmentation. There are several variations available of this approach. The traditional active contour approach was proposed in [33].

A modified version of the traditional active contour approach is proposed in [34] and it is known as geometric active contours. This approach uses gradient information of an image to construct the edge stop function.

A region information-based approach is proposed in [35]. This approach is developed by Chan and Vese and this is a parametric representation. Some deep learning approaches are developed that use the loss function of the active contour model as their loss function [36].

Although the mortality rate is not very high, the highly infectious nature of this virus is the main threat to society. Due to the absence of any specialized drug, it is very difficult to restrict the drastic spread of this virus.

Apart windows 10 1703 download iso italy covid cases – windows 10 1703 download iso italy covid cases using various protective equipment, early detection and isolation can be very effective to combat the spread of this highly infectious virus. In the middle of this pandemic scenario, some vaccines are invented and are being applied to the people and it is a ray of hope to fight against this virus. As per the report of the world health organization, , numbers of confirmed cases ссылка на продолжение be observed in countries and 4, people are already expired due to this disease as of 15th Octoberpm CEST [40].

From these statistics, it is clear that the worldwide mortality rate is approximately 2. The major risk factor lies in the highly infectious nature of this virus. Hopefully, 6,, vaccine doses have already been administered worldwide which may be helpful in reducing ссылка на подробности mortality rate.

Many countries are not prepared with the appropriate infrastructures to support COVID infected patients. Moreover, many people from remote areas are not even able to arrange protective источник статьи like masks, sanitizers, etc. The reverse transcription-polymerase детальнее на этой странице reaction test i.

It is a quite inspiring finding because CT scan images can be used to isolate some suspected patients at an early phase жмите сюда therefore, the drastic spread of this virus can be stopped to some extent. The presence of some prominent features like ground-glass opacities, crazy paving, etc. Typically, the absence of properly annotated data makes the automated biomedical image analysis job difficult.

As the name suggests, the proposed approach is based on the superpixels and type 2 fuzzy systems where the type 2 fuzzy objective function is modified to incorporate the advantages of superpixels to efficiently process a large amount of spatial information. The fuzzy objective function is optimized with the recently developed metaheuristic procedure i. The proposed method allows automated and efficient analysis of the CT scan images which is beneficial to enhance the computer-aided diagnostic systems to act as a tool against the COVID virus.

To summarize, the major contributions are as follows: 1 A novel superpixel-based image segmentation technique is proposed that reduces the incurred computational cost for processing a high amount of spatial information, 2 Type-II fuzzy system is incorporated with the superpixel-based approach, 3 A recently developed metaheuristic procedure ACSO is further enhanced, 4 The conventional fitness function of the FCM clustering approach is enhanced to exploit the advantages of superpixel 5 The cluster centers are updated with the help of the proposed fuzzy ACSO approach.

The remaining article is prepared in the following way: Sections 23 describes the artificial cell swarm optimization method and the type 2 fuzzy clustering framework respectively. Section 6 discusses some of the relevant points and a brief conclusion is presented in Section 7. This is a recently developed metaheuristic procedure that is inspired by the artificial cell division procedure.

The artificial cell swarm optimization procedure mimics the artificial cells as the search agents. The actual artificial cell division approach [46] is slightly modified to design the optimization procedure. The incorporated modifications are listed below [47] :. The hierarchical tree structure is formed throughout the generations due to the artificial cell division process. Swarms of artificial cells are considered in the optimization process to take part in the artificial cell division process.

No communication is allowed between any pair of artificial cells. Lifespan of the k th artificial cell at a certain timestamp t s is an important parameter and it is directly dependent on the fitness value f i t n e s s k as given in Eq. A huge number of swarms can significantly increase the fitness evaluations and a small number of swarms can increase time to converge and therefore, is essential to decide принимаю.

windows 10 1703 iso ita download windows mediatakeout мысль swarm count moderately. In this work, the swarm count is considered is a fixed parameter. One artificial cell can produce some new cells and the production of new cells occur at a certain distance which is inversely dependent on the fitness of the producer cell as expressed in Eq.

The distance between the k th cell and any of the l th cell, which are produced from the same parent cell, must be same. Therefore, if a cell is near to global optima, then it can generate some other cells at a ссылка на страницу distance and vice-versa. Smaller steps help to search the nearest portions of the global optima cautiously so that the global optima may not be missed accidentally.

A cell does not have any effect on the population once its lifespan is over. This property helps to maintain the size of the population and prevents getting overpopulated. The successor cells of a cell can produce some other cells by the cell division process to maintain the population. The life span of a cell can belong it belongs to the near-optimal area. The quality of a population is evaluated using the lambda function which is given in Eq.

Algorithm 1 illustrates the artificial cell swarm optimization approach in brief [47]. The proposed approach adopts the type 2 fuzzy logic-based clustering approach to effectively model and handle the random uncertainties. In most real-life applications, the uncertainty cannot be predicted in advance. A wide range of input types can produce random uncertainties. Hence, it is essential to cope up with the random uncertainties in real-life scenarios. The fuzzy C-means clustering approach is one of the widely used clustering approaches which is suitable to various problems of different domains [48][49][50][51].

The main reason behind the increasing popularity of fuzzy systems is the suitability of this approach in different scenarios where the crisp clustering approaches do not perform well. A single point can be a member of more than one cluster at the same time with some membership values.

The total sum of all membership values for a certain point must be one. So, the value of the membership can be anything between нажмите чтобы узнать больше and 1. The dissimilarity function which is optimized by the fuzzy C-means clustering approach is given in Eq. The cluster centers can be updated using Eq. The type 2 fuzzy logic systems use separate sets of membership values that are also fuzzy in nature. This approach allows efficient modeling of dynamic input uncertainties by providing additional degrees of freedom.

In this work, the type 2 fuzzy logic-based clustering approach is adopted to overcome some of the common problems of type 1 fuzzy systems like noise sensitivity, relative membership values, etc.

It is essential to improve the outcome of the segmentation process. The uncertainty of a point must be decided depending on the membership value i. So, a lower membership value indicates higher uncertainty and vice-versa.

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