Many software reliability growth models (SRGMs) have been analy zed for measuring the growth of software reliability. Selection of optimal SRGMs for use in a particular case has been an area of interest for researchers in the field of software reliability. A ll existing methodologies use same weight for each comparison criterion. But in reality, it is the fact that all the parameters do not have the same prio rity in reliability measurement. Keep ing this point in mind, in this paper, a co mputational methodology based on weighted criteria is presented to the problem of performance analysis of various non-homogenous Poisson process (NHPP) models. It is relat ively simple and requires less calculation.A set of twelve comparison criteria has been formulated and each criterion has been assigned different weight to rank the software reliability growth models proposed during the past 30 years. Case study results show that the weighted criteria value method offers a very pro mising technique in software reliability growth models comparison. Index Terms-Srg ms, Soft ware Reliab ility, NHPP, Software Model Ranking Analysis and Ranking of Software Reliab ility Models Based on Weighted Criteria Value
Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder—Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network’s depth.
The study is presented in four sections. The first section defines the municipal solid waste and solid waste management system. The second section illustrates the descriptive statistical analysis of waste generation patterns in India. The average waste generation in India was 160,038.9 tons per day in 2021; 95% of this total waste was collected and transported to the disposal sites. Based on scientific studies and observations, the per capita waste generation rate in 2018 was 0.490–0.626 g per day. In the last one and a half decades (1999–2000 to 2015–2016), Delhi and Bangalore have shown the highest percentage growth of 2075% and 1750%, respectively, in total waste generation among the highest population cities. The analysis of waste generation patterns concludes urbanization is a major factor that highly influences the waste generation rate. The third section describes the major issues in current solid waste management services. Some of these issues are the unavailability of web portals for citizens, no real-time monitoring of bins, collection vehicles and illegal dumping. These issues are identified based on the survey performed in a city and analysis of related research studies and scientific reports. We determined that illegal dumping is one of these major concerns and needs a technological solution. In the fourth section, we propose a multipath convolutional neural network (mp-CNN) to detect and localize the waste dumps on streets and roadsides. We constructed our dataset to train and test the proposed model, as no benchmark dataset is publicly available to obtain this objective. We applied the weakly supervised learning approach to training the model. In this approach, mp-CNN was trained according to the image class; in our case, it is two (waste and non-waste). In the testing phase, the model showed the performance evaluation matrices 97.82% of precision, 98.86% of recall, 98.34% of F1 score, 98.33% of accuracy, and 98.63% of AUROC for this binary classification. Due to the scarcity of benchmark datasets, waste localization results cannot be presented quantitatively. So, we performed a survey to compare the overlapping of the mask generated by the model with the region waste in the actual image. The average score for the generated mask obtained a score of 3.884 on a scale of 5. Based on the analysis of model performance evaluation parameters, precision-recall curve, receiver characteristic operator curve, and comparison of mask generated by the model over waste with corresponding actual images show that mp-CNN performs remarkably good in detection, classification, and localization of waste regions. Finally, two conceptual architectures in the context of developing countries are suggested to demonstrate the future practical applications of the mp-CNN model.
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