The deployment of 5 G wireless technology has generated considerable interest and debate regarding its potential effects on human health. This work provides a comprehensive overview of the current scientific understanding of the potential health implications associated with 5 G technology. Drawing upon a wide range of research studies, reviews, and expert opinions, we explore the implications through which 5 G signals interact with the human body. This work presents a balanced perspective, summarizing both the potential benefits of 5 G technology, such as improved data transfer speeds, reduced latency, and enhanced connectivity, as well as concerns that have been raised about its effects on human’s tissues. We discuss various aspects of health impacts, including thermal and nonthermal effects, focusing on the existing research on radiofrequency electromagnetic fields and their potential to cause adverse health outcomes. Simulation results show the negative effect of radio waves on human’s tissues.

Sentiments and emotions play a pivotal role in our daily lives. They assist decision making, learning, communication and situation awareness in human environments. Sentiment analysis is mainly focused on the automatic recognition of opinions’ polarity, as positive or negative. Nowadays, sentiment analysis is replacing the old web based survey and traditional survey methods that conducted by deferent companies for finding public opinion about entities like products and services in order to improve their marketing strategy and product of advertisement, at the same time sentiment analysis improves customer service. Large number of videos is being uploaded online every day. Video files contain text, visual and audio features that complement each other. Multimodality is defined by analyzing more than one modality, Multimodal Sentiment Analysis refers to the combination of two or more input models in order to improve the performance of the analysis; a combination of text and audio-visual inputs is an example. The automatic analysis of multimodal opinion involves a deep understanding of natural languages, audio and video processing, whereas researchers are continuing to improve them. This paper focuses on multimodal sentiment analysis as text, audio and video, by giving a complete image of it and related dataset available and providing brief details for each type, in addition to that present the recent trend of researches in the multimodal sentiment analysis and its related fields will be explored.

One of the biggest problems with Internet of Things (IoT) applications in the real world is ensuring data integrity. This problem becomes increasingly significant as IoT expands quickly across a variety of industries. This study presents a brand-new data integrity methodology for Internet of Things applications. The “sequence sharing” and “data exchange” stages of the suggested protocol are divided into two parts. During the first phase, each pair of nodes uses a new chaotic model for securely exchanging their identity information to generate a common sequence. This phase’s objectives include user authentication and timing calculations for the second phase of the recommended method’s packet validation phase. The recommended approach was tested in numerous settings, and various analyses were taken into account to guarantee its effectiveness. Also, the results were compared with the conventional data integrity control protocol of IoT. According to the results, the proposed method is an efficient and cost-effective integrity-ensuring mechanism with eliminates the need for third-party auditors and leads to reducing energy consumption and packet overhead. The results also show that the suggested approach is safe against a variety of threats and may be used as a successful integrity control mechanism in practical applications.

Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attack based on clustering and classification ML methods (CMLM): Deep Learning (DL) and Decision Tree (DT). Simulation results show that the proposed technique achieves high percentage of accuracy in detecting phishing

The main goal of Mobile Ad hoc Network (MANET) is connecting mobile devices directly without central point (peer to peer), which makes it popular area of research. MANET is running using routing protocols that enable mobile devices (nodes) to communicate to each other. Different types of routing protocols have been designed that depend on different strategies in order to establish and maintain connections between mobile nodes. Nowadays, Artificial Intelligence (AI) has involved in all fields of science and technology aiming to enhance the performance and gaining accurate results. This research aims to design and implement a wireless routing protocol for MANET based on AI (Ant-OBS - Ant on Behalf of Source) in order to enhance the performance of network in terms of network overhead and power consumption. The proposed protocol benefits from the behavior of ant insect. A mathematical model is designed to model the proposed protocol; the output of the model performs well when it is compared to AODV and LAR1 routing protocols.

Segmentation of audio data such as human speech (splitting each word in separate audio file – .WAV file) has been a major concern when working with multimedia such as recordings from radio or TV. The main focus of the segmentation of boundaries of spoken language has been on using energy and zero crossing thresholds for endpoint detection. Errors in endpoint detection are still a main cause of low accuracy of segmentation systems. The goal of this research is to develop an efficient algorithm in order to segment the speech of human in both languages of English and Arabic in different speaking speed with high accuracy. Simulation results show that the developed algorithm achieved high accuracy when segmenting human speech in English language up to 91.6% in average, while it is 89.0% of Arabic language.