For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. The entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention to achieve efficient data communication towards the sink node via selected CH and increase the node reachability within the Cluster. An energy-efficient deep belief network (DBN) based routing protocol is developed in this proposed framework, which achieves better data transmission through the selected path. Due to this, the packet delivery ratio (PDR) gets improved. In this framework, the nodes in the whole network are initially grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes belonging to the particular Cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. Finally, the performance of the proposed deep network-based routing protocol is evaluated using different evaluation metrics: network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with a few existing algorithms. The proposed DBN routing protocol has achieved a better network lifetime among all these algorithms.

In contemporary society, the proliferation of online hateful messages has emerged as a pressing concern, inflicting deleterious consequences on both societal fabric and individual well-being. The automatic detection of such malevolent content online using models designed to recognize it, holds promise in mitigating its harmful impact. However, the advent of "Hateful Memes" poses fresh challenges to the detection paradigm, particularly within the realm of deep learning models. These memes, constituting of a textual element associated with an image are individually innocuous but their combination causes a detrimental effect. Consequently, entities responsible for disseminating information via web browsers are compelled to institute mechanisms that regulate and automatically filter out such injurious content. Effectively identifying hateful memes demands algorithms and models endowed with robust vision and language fusion capabilities, capable of reasoning across diverse modalities. This research introduces a novel approach by leveraging the multimodal Contrastive Language-Image Pre-Training (CLIP) model, fine-tuned through the incorporation of prompt engineering. This innovative methodology achieves a commendable accuracy of 87.42%. Comprehensive metrics such as loss, AUROC, and f1 score are also meticulously computed, corroborating the efficacy of the proposed strategy. Our findings suggest that this approach presents an efficient means to regulate the dissemination of hate speech in the form of viral meme content across social networking platforms, thereby contributing to a safer online environment.

Liver cirrhosis is a terminal pathological result of chronic liver damage, illicit drugs, hepatotoxicity, and non-alcoholic steatohepatitis. Assessment of liver cirrhosis via non-invasive methods in order to circumvent the limitations of liver biopsy. This research builds upon the growing body of knowledge in liver cirrhosis assessment by examining a dataset comprising cases of primary biliary cirrhosis. Prior research has primarily concentrated on comparative analyses and development of machine learning models integrating imaging modalities such as ultrasound, magnetic resonance imaging (MRI), and elastography, with limited focus on serum biomarkers. This research endeavors to address the neglected aspects of liver cirrhosis assessment by leveraging Explainable AI algorithm to bridge the gap between AI models and human comprehension via providing insights into the intricate decision-making process of the proposed machine learning model, thus enhancing transparency and trustworthiness. This novel approach aims to overcome the limitations of previous works and contribute to improved liver cirrhosis diagnosis.

There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. The traditional solar energy cell's inability to create power in the evening is a horrible flaw. This investigation focuses mostly on solar-powered energy and discusses its evolution, improvements, and future perspectives.