The change in habits and lifestyle of citizens during health crisis like COVID-19 pandemic has resulted in an unprecedented increase in the struggles for solid waste management across the globe. Not only underdeveloped and developing economies are struggling with the challenges posed by mounting piles of infectious waste but even developed countries are adversely affected in similar manner. The routine waste management strategies followed by various countries are overturned due extremely altered trends in the amount and type of waste generated by households and medical facilities. The aim of this paper is to study and list the best available waste management policies adopted by some developing, developed and underdeveloped economies. The listed case studies were selected due to some unique steps undertaken for solid waste disposal during pandemic. The findings revealed that the guidelines issued by WHO for waste management of corona virus infected waste were followed by these nations and certain additional preventive steps were taken. Due to unavailability of single framework as prescribed by international authorities, various sustainable steps taken by individual countries to curb the pandemic menace can be useful in the present context. Few of these measures can be permanently adopted at global level by other nations for handling the pandemic like situations efficiently in pandemic situations.

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

In the modern world, due to the usage of high-power chemical-based cosmetics, climate change, and other major factors, skin cancer has been increasing among individuals. Skin cancer is considered as the most common malignant disorder, and there are more than a million cases being recorded with this disease every year. Extensive studies have already been performed to identify the risk factors and causative agents for skin cancer, including lifestyle changes and eatery patterns among individuals. The most common type of skin cancer is classified into basal cell carcinoma and squamous cell carcinoma. The researcher intends to conduct the research with the primary goal of determining the important factors in blockchain technology in the treatment of skin cancer in senior people. The application of new technologies such as blockchain has enabled offering better promises to health care professionals in addressing skin cancer in a more effective manner. These tools supported in evaluating the nature and severity of psoriasis has been regarded as much support for health care professionals in detecting skin cancer and offer better health care guidance for better living. The detection of melanomas supports the patient in enhancing the prognosis and support in discriminating between the melanomas and less impact lesions. The blockchain-based classification system offers more benefits and reduces the cost of detecting skin cancer in an effective manner. It also helps the medical professionals by assisting them in developing a custom diet plan for each patient on the basis of their health records and food intake. The researchers are focused on applying both the primary data sources and secondary data sources for performing the study. A detailed questionnaire is designed, and it is shared with the participants through university hospitals, support groups, etc. so as to gather the information. Nearly 156 respondents were chosen through nonprobability sampling, and the information was collected. The researcher performs critical descriptive analysis, and correlation analysis is performed to understand the overall association between the variables. The researchers intend to perform the study with the basic goal of understanding the critical factors in blockchain technology in skin cancer for elderly individuals. The major factors involved are enhanced data privacy, support in forecasting patterns, and enhanced medical services to patients complemented with personalized dietary assessment and recommendations. The result demonstrates that artificial intelligence-based blockchain technology allows for the efficient processing of huge amounts of data in order to complete the assigned task and correctly determine and predict the model.