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.

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.

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image–patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image–patch thermograms.

The possible threats that healthcare waste management (HWM) poses to the environment and public health are making it more and more crucial for medical facility administrators to be worried about it. This is in line with the global trend towards firms giving sustainability more of a priority. Many organizations, including the World Health Organization (WHO) and other organizations, as well as national and state laws, have mandated the proper disposal of infectious and hazardous healthcare waste. To effectively address the complex problem of selecting the best treatment option for HWM, a multi-criteria decision-making (MCDM) procedure must be used. The alternative ranking order method accounting for two-step normalization (AROMAN) methodology is provided in the context of q-rung orthopair fuzzy environment. This method comprises two steps of normalization and is based on the criteria importance through intercriteria correlation (CRITIC) paradigm. Whereas the AROMAN methodology uses vector and linear normalization techniques to improve the accuracy of the data for further computations, the CRITIC method assesses the intercriteria correlations and scores the significance of each criterion. The ranking from the proposed method is $A{l}_5>A{l}_4>A{l}_3>A{l}_1>A{l}_2$. The study’s conclusions indicate that recycling ($A{l}_5$) is the best option since it lessens trash production, aids in resource recovery, and protects the environment. Using this method helps decision makers deal with subjectivity and ambiguity more skillfully, promotes consistency and transparency in decision making, and streamlines the process of choosing the best waste management system. Sustainable waste management practices have been implemented in the biomedical industry with some success. The proposed technique is a helpful tool for legislators and practitioners seeking to improve waste management systems.

In response to the transportation industry’s immediate need for more proactive and foresighted management, cooperative intelligent transportation systems (C-ITS) use wireless technology to foster real-time communication between vehicles and infrastructure, with the goal of increasing road safety and streamlining traffic flow. The introduction of self-powered sensors, which have the clever capacity to produce their own electricity, is critical for the reliability of transport operations across a wide range of C-ITS applications. Our analytical technique, which employs the criteria importance through intercriteria correlation (CRITIC) methodology and the weighted aggregated sum product assessment (WASPAS) approach, does not end there. We have taken it a step further by using the T-spherical fuzzy (T-SF) form of CRITIC-WASPAS, which adds a more subtle layer to our evaluation. We embark on an in-depth case study, methodically evaluating five choices against eight criteria. In the context of the CRITIC-WASPAS framework, the application of the T-SF logic enhances analytical precision by taking into account uncertainty throughout the decision-making process. This is particularly beneficial in the context of complex systems like C-ITS. The incorporation of CRITIC also makes it possible to evaluate the correlations between different criteria, which ultimately results in decision outcomes that are more nuanced and robust. This is accomplished by aggregating all of the criteria and options. This rigorous and intelligent strategy goes beyond simply meeting urgent system needs. It tactically impacts decision-making, moving the transport industry forward significantly.

The rapid advancement of computer technology and the widespread adoption of online social media platforms have inadvertently provided fertile ground for individuals with antisocial inclinations to thrive, ushering in a range of security concerns, including the proliferation of fake profiles, hate speech, social bots, and the spread of unfounded rumors. Among these issues, a prominent concern is the prevalence of hate speech within online social networks (OSNs). However, the relevance of numerous studies on hate speech detection has been limited, as they primarily focus on a single language, often English. In response, our research embarks on an exhaustive exploration of multilingual hate speech across 12 distinct languages, offering a novel approach by adapting hate speech detection resources across linguistic boundaries. This study presents the development of a robust, lightweight and multilingual hate speech detection model, known as MLHS-CGCapNet, which combines convolutional and bidirectional gated recurrent units with a capsule network.With commendable accuracy, recall and f-score values of 0.89, 0.80, and 0.84, respectively, our proposed model exhibits strong performance, even when handling an imbalanced dataset. Notably, during the training and validation phases, the suggested model showcases exceptional effectiveness, achieving accuracy values of 0.93 and 0.90, respectively, particularly in the challenging context of imbalanced data. In comparison to both baseline and state-of-the-art techniques, our model offers superior performance.