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The concept of the Internet of Things (IoT) is significant in today's world and opens up new opportunities for several organizations. IoT solutions are proliferating in fields such as self-driving cars, smart homes, transportation, and healthcare, and new services are constantly being created. Over the previous decade, society has seen a significant expansion in IoT connectivity. In reality, IoT connectivity will expand in a variety of domains over the next few years. Various problems must be overcome to permit effective and secure operations. However, growing connections increase the potential for cyber-attacks since attackers can exploit the broad network of linked devices. Artificial intelligence (AI) detects and prevents cyber assaults by constantly developing and adjusting to new threats and weaknesses. In this study, we offer a novel cyber-detection model for IoT networks based on convolutional neural networks (CNN) transformers. The study aims to enhance the system's ability to identify and detect cyberattacks, new and sophisticated assaults, and its performance. The experimental study findings, using a new cybersecurity CICIoT2023 dataset, show that the CNN-Transformer model can detect IoT hazards with an overall accuracy of 99.49%. In identifying hazardous activity, MLP accuracy is 99.39%, while XGBoost-pipeline accuracy is 99.40%. [ABSTRACT FROM AUTHOR]

A longer battery life is a highly sought-after feature for most smartphone users when considering their next device. However, with the emergence of new hardware technology and software applications that require heavy processing, the demand for battery power has significantly increased. Unfortunately, the development of battery technology has not kept up with the rapid advancements in smartphone hardware and software, which rely heavily on battery power. To address this issue, several approaches have been proposed to regulate battery consumption and the charging process on smartphones. In this paper, we summarize the different approaches related to this problem that managed to achieve up to a 61% increase in battery daily usage in simulation testing, highlighting their strengths, limitations, and current challenges. Furthermore, we provide a comprehensive review of various open-source datasets that have the potential to be used in developing new approaches to improve battery drainage and degradation in smartphones. We also discuss the methodology for collecting each dataset. Finally, we propose a new approach to address the current limitations and challenges to solving the problem of battery drainage and degradation that could be developed using the currently available datasets. These new approaches may involve incorporating Machine Learning (ML) techniques to predict battery charging patterns and minimize battery drainage. [ABSTRACT FROM AUTHOR]

Smartphones are mobile devices that can connect to the Internet through various means such as Wi-Fi, cellular data networks (3G, 4G, 5G), or even through tethering to another device. Once connected to the Internet, smartphones can access a wide range of online services and applications, including web browsing, social media, email, streaming videos, online gaming, and much more. Malware attacks have significantly increased as a result of data movement. Malware causes unexpected smartphone behavior, including changing phone bill charges, intrusive advertisements, confusing messages being sent to contacts, unreliable performance, the appearance of new apps, unusual data use, and a noticeable drop in battery life. But smartphone consumers are still vulnerable to malware attacks. To solve this problem, we created a Malware detection system. Malicious Android Apps are categorized using static analysis through the APK's metadata file. "Drebin" dataset primarily uses the Android manifest file as one of the key features for Android malware detection. Additionally, we investigated algorithms for static analysis, including adaboost, ANN, decision trees, extra trees, K-nearest neighbors, lasso regression, logistic regression, MLP, naïve bayes, random forests, ride regression, support vector machines and XGB. We employ "Drebin" dataset with different feature extractors to reduce dataset dimensionality. We use TF-IDF and word2vec feature extractor. The experimental results show that TF_IDF performs better on "Drebin" dataset. [ABSTRACT FROM AUTHOR]

Humans can be recognized by their distinctive walking patterns, which have been established using a variety of techniques, including the use of sensors. Footstep recognition, which analyzes the distinctive characteristics of a person's footsteps, can be applied in a range of scenarios, including security, criminal investigations, human behavior security applications, and healthcare for monitoring and analyzing gait abnormalities. This paper discusses the most recent work on footstep analysis and identification systems in terms of using the floor-based approach. It explains the various artificial intelligence methods as well as the machine learning and deep learning algorithms applied to the recognition and analysis of footsteps, the various feature engineering techniques applied to each type of sensor, the affection of the engineered features on the footstep identification and analysis systems, and the best suitable features for each type of sensor and application, which provide researchers in this domain with an appropriate grounding in footstep identification and analysis utilizing the floor-based technique. [ABSTRACT FROM AUTHOR]

Routing process is one of the most critical processes in wireless sensor network (WSN). Due to WSN is mainly used in many applications in internet of things (IoT), routing algorithm can affect the performance of these applications. Thus, the usage of inefficient routing algorithm may lead to losing the data collected by sensors. Moreover, it will cause the sensors to waste energy. This paper proposes an energy efficient clustering-based routing algorithm that is based on tunicate swarm algorithm (TSA). TSAbased clustering algorithm selects the optimal cluster head by calculating the remaining energy, the distance to the base station (BS), the distance to each cluster member and balancing the load between the created clusters. TSAbased routing algorithm is used to create paths from cluster heads to the BS using relay nodes. The TSA-based routing algorithm creates the paths based on the path length, the count of relay nodes in the path, and the number of cluster members of each relay node. The result shows that the proposed algorithm is promising in respect of extending the lifetime of the network and conserving the energy. [ABSTRACT FROM AUTHOR]

Medical imaging is a crucial tool for various clinical applications, including examination of medical issues, including early identification, monitoring, diagnosis, and therapy. To analyse medical images using computer vision, it is crucial to comprehend the ideas behind artificial neural networks and deep learning. In this particular article, we present recent principles of deep learning models and the different forms of widely used activation functions. Utilising Deep Learning Approach (DLA) has expanded quickly in the study of medical images, particularly in detecting either being present or not diseases. This study explores artificial neural networks development and provides a comprehensive analysis of DLA and potential uses for medical imaging. The majority of DLA implementations focus on pictures from digital histopathology, computer tomography, mammography, and X-rays. A thorough overview of the literature on the classification, detection, and segmentation of medical pictures using DLA is provided in the paper, which might help researchers decide what changes should be made to medical image analysis. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are an essential part of the Internet of Things (IoT). Indeed, the usage of efficient routing algorithms makes IoT applications work better. Since sensors are connected to limited sources of energy, some sensor nodes lose energy in a short time. This can affect the network's lifetime. This paper proposes a routing algorithm that works on extending the network lifetime. The proposed algorithm uses Tunicate Swarm Algorithm (TSA), which is a new bio-inspired algorithm. TSAbased clustering is used to select the best cluster heads. Many parameters are considered while selecting the optimal cluster heads such as distance and energy parameters. TSA-based routing is used to create efficient paths from the cluster head to the base station. The path length and the number of hops in the path are considered during creating the paths. The proposed algorithm is compared with three of the most used metaheuristic-based routing algorithms like Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO). The comparison evaluates the performance of the TSA-based routing algorithm. TSA-based clustering is used with all the algorithms that are compared. The comparison proves that the proposed algorithm extends the lifetime of the network more than the other algorithms. The time before half of the nodes were dead was extended to be 3.17% more than PSO and GWO, and 1.36% more than ACO. [ABSTRACT FROM AUTHOR]

The health support of everybody should be considered as a highly significant, due to the huge increases in various health concerns. Meanwhile, heart attacks and cardiovascular disorders caused a public issue with high mortality ratios and cardiac patients, thereby increasing the open-heart surgery cases, limiting the available number of doctors, and most importantly generating an inefficient environment for caring patients, particularly with severe and fragile health problems. Hence, healthcare systems have begun to connect with IoT to keep each patient's digital identification. We, therefore, aimed at designing a smart device for cardiac patient's monitoring to save time and effort for both caregivers and patients. Our design also could prevent crowding in health care places, provide a link between a person's hectic lifestyle and regular, and continuous the health checkups via remote access. The results from our prototype device can be employed for cardiac patient monitoring using IoT-technique which will monitor several health signs, i.e., blood oxygen ratios, heart rate, and body temperature. These signs can be remotely retrieved and/or imagined via medical experts on a handheld device through blynk APP from any location at any time. They also can gather the data and provide the real-time information about the patient through the Bluetooth module. Several individuals were put through a series of tests based on a variety of parameters. In conclusion, IoT could be recommended as a smart tool in the health care for monitoring the cardiac patients quickly and efficiently. [ABSTRACT FROM AUTHOR]

Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient's care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients' data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital's data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system. [ABSTRACT FROM AUTHOR]

Computer music generation systems aid musicians in creating and producing high quality music using computers. In the few past years, various intelligent techniques have been used to teach computers ways of generating music either through composition, improvisation, or expressive music playing. This paper presents a comprehensive analysis of the recent publications in music generation field. We particularly focus on music composition applications, especially those adopting algorithmic composition techniques. Our analysis covers the most recently adopted techniques such as deep neural networks and generative adversarial networks. The importance of our analysis is to give insight to researchers of this area about the most suitable techniques to choose from to perform each musical task. Our study shows that the most successful commonly used technique for improvisation is factor oracles. As for music expressiveness which is a rather new field, mathematical models and neural networks achieved the best results. Composition systems have various branches; however, the state-of-the-art technique for composition applications is the generative adversarial networks. [ABSTRACT FROM AUTHOR]

Cloud computing is the new paradigm of representing computing capabilities as a service. With its facility of resource sharing and being cost effective, it exists in every domain of life enhancing their functionality and adding new opportunities to it. Accordingly, the focus towards solving its' dilemmas like load balancing becomes more challenging and the research of the new intelligent Algorithms to find optimal results has been expanding. This paper discusses the use of two newly emerged swarm algorithms including: Ant-Lion optimizer (ALO) and Grey wolf optimizer (GWO) in task scheduling of the Cloud Computing environment. Additionally, compare the results with commonly known swarm algorithms Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA).The results show the ALO2 and GWO are a strong adversary to Particle Swarm Optimization (PSO), and better than Firefly (FFA) and they have potential in load balancing. [ABSTRACT FROM AUTHOR]