Journal of Intelligent Systems and Internet of Things

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

This research presents an effective web system for weather monitoring based on the Internet of Things (IoT) and cloud computing. The three primary parts of the system are an online application, a cloud platform, and Internet of Things-based weather stations. Periodically, IoT-based weather stations gather meteorological data and send it to a cloud platform. The ANN model can access the meteorological data that is stored in a database by the cloud platform. ANN model utilizes the meteorological data to produce predictions for several weather factors. The web application gives users access to real-time weather data and forecasts.

Dynamic voltage and frequency scaling (DVFS) is a tool used primarily to decrease computer processor energy consumption by lowering its operational frequency. Their only downside is that they distract from the efficiency of parallel applications while operating on parallel platforms. In a heterogeneous cluster architecture, however, a genetic algorithm is being implemented and applied to model the best trade-off between energy-saving and parallel application performance degradation. The proposed algorithm selects the best frequency vector in order to accomplish these objectives by providing the same compromise. So, the objective function of the genetic algorithm at the same time gives limited energy consumption and minimum decreases in performance. The SimGrid simulator will be used for all experiments. The suggested algorithm saves the average energy by (20 %) and the application performance degrades to the limit (0.15 %).

Interest in wearable technology and the need for eco-friendly solutions have spurred new methodologies. This research examines how sophisticated deep learning and biomimetic designs benefit each other. The results may change smart technology forever. The introduction highlights the global appeal of wearable technology and the importance of environmental considerations in design. Deep learning and biomimicry are a fresh and exciting combination that might increase smart device accuracy, energy efficiency, and biomimicry. This project seamlessly integrates biomimetic design elements with deep learning methods. Biomimicry affects wearable technology design and functioning. However, deep learning techniques based on artificial neural networks boost user flexibility and predictive analytics. The controlled experiment allows a thorough examination of a number of datasets designed to cover a wide range of biomimetic settings and user behaviours. The data prove that the proposed technique beats alternatives across several performance parameters. Integrating biomimetic principles with deep learning systems boosts accuracy. This proves the system's reliability. The biomimetic method is eco-friendly since energy efficiency grows dramatically. Biological mimicry indications show that the suggested strategy resembles natural systems. A new exploratory method enhances sustainable technologies. Integrating biomimicry and deep learning efficiently enhances gadget performance and meets environmental standards. This research emphasizes the transformational power of nature-friendly technology, changing our worldview. Our study helps ensure that upcoming wearable technologies are cutting-edge and ecologically beneficial. Deep learning and biomimetic designs are converging, marking a tipping point in sustainable technology. This helps move toward an eco-friendly future.

The game's physical and physiological stakes are equal for all players. The two dimensions that rely on the power of physical and physiological consequences are the pursuer and the defence. Whether a chaser or defender, male or female, the physiological actions that occur during the physical activity will have a good effect on the body and on the personality. A Wireless Body Area Network (WBAN) is a network that may transmit real-time traffic like data, speech, and video to monitor the state of essential organs capabilities while remaining external to the body. The present research provides a clear evaluation of how different bones and muscles function, metabolism, movement regulation, and energy generation in relation to varying environmental conditions. There are physiological differences between a chaser and a defender. The primary goal is to gain an in-depth IoT based understanding of how several physiological variables, such as resting heart rate, maximum heart rate, aerobic capacity, and the regulation and maintenance of red blood cells and haemoglobin, are affected by skeletal muscle contraction. It was discovered based on artificial intelligence that the defenders with high speed agility and flexibility performed better in the pre-test. Physiological variables have a considerable impact on speed, strength, agility, and flexibility tests.

An architecture for a wireless network that is constantly developing, decentralized, and multi-hop is called a mobile ad hoc network. MANETs are able to function in many different contexts where regular networks are unable. As can be seen from the advantages listed above, these networks are well-suited for a wide variety of applications, some of which include military and commercial use, as well as applications relating to disaster management, rescue operations, and defense. Energy conservation is a standard factor that indicates the overall network lifetime in mobile ad hoc networks that operate on rechargeable or replaceable battery. This is because usage, battery power consumption in relation to transmission range, type of application running on each device, location, and other influences all play a part in determining the overall network lifetime. An earlier study used a method called ant colony optimization, which is a form of swarm intelligence enthused by the activity of foraging ants in colonies. The best possible travel plan was found using this strategy. Current MANETS routing systems face difficulties in load balancing and energy efficiency that must be overcome if optimal path convergence is to be achieved. When deciding on the next hop node, the IoT based AEEBLR method is recommended. The latency, energy consumption, congestion, and connection quality are all taken into account before making a final decision. The likelihood of selecting the next-hop node as the neighbor node is determined using these metrics. It is the following hop's probability that determines which ant agent goes forward and which goes backward. This paves the door for the creation of many paths, from which the most effective might be chosen for transmission. The results of the implementation show that the suggested AEEBLR technique outperforms the existing AESR approach when the number of packets, the number of nodes, and the mobility of nodes are all varied.

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

The amalgamation of wearable sensor technologies and artificial intelligence (AI) presents a transformative paradigm for optimising athletic performance in real time. This paper explores the integration of cutting-edge sensors - including bioimpedance sensors, accelerometers, and gyroscopes - with advanced AI algorithms such as machine learning and decision support systems. By capturing diverse physiological, biomechanical, and environmental data, the proposed framework aims to offer personalized, actionable insights for athletes. This research synthesizes the current landscape of wearable sensor technology in sports and highlights the evolving role of AI in interpreting data for enhancing athletic performance. It delineates an innovative framework designed for real-time analysis, personalized feedback, and training optimization. The seamless interaction between sensors and AI models empowers athletes and coaches to make informed decisions, optimizing training regimens and minimizing injury risks. The paper discusses the practical implications, challenges, and ethical considerations associated with this integration, emphasizing its potential benefits in diverse sports disciplines. Results from real-world trials underscore the efficacy of the proposed framework in providing dynamic guidance to athletes, thereby augmenting their performance through tailored interventions.

New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks.

Key challenges in Wireless Sensor Networks (WSNs) include reduced dormancy, energy efficacy, reportage worries, and network lifetime. To solve the issues of energy efficiency and network longevity, more study of cluster-based WSNs is required. In order to address the challenges and constraints of WSNs, creative approaches are needed. WSNs use machine-learning techniques because of their unique characteristics. These characteristics include high communication costs, low energy reserves, high mobility, and frequent topological shifts.  The current method picks cluster heads at random at the beginning of each cycle, not considering the remaining energy of these nodes. It is possible that the newly chosen CH nodes will have the lowest energy level in the network and will die off fast as a result. Energy is wasted while communicating over long distances between cluster heads and the BS, which occurs frequently in a big network due to Internet of things. This would mean that WSNs have a finite lifespan. Therefore, to increase the network's longevity and efficiency, we propose a machine-learning-based strategy called energy proficient crossbreed clustering methodology (ECCM). The experimental results reveal that the ECCM is superior to the LEACH approach, increasing residual energy by 35%, extending network lifetime by 37%, and increasing throughput by 15%.  

This research introduces an advanced network security methodology based on IoT, combining five innovative algorithms: Dynamic Threat Detection (DTD), Adaptive Intrusion Prevention System (AIPS), Anomaly-Based Security Metrics (ABSM), Context-Aware Firewall (CAF), and Cognitive Security Assessment (CSA). Each algorithm contributes specific functionalities, ranging from real-time threat detection and adaptive policy adjustments to anomaly quantification, contextual rule modifications, and holistic security risk assessments. The ablation study conducted on each algorithm reveals critical components driving their performance, ensuring a deep understanding of their inner workings. The proposed method demonstrates superior performance in accuracy, scalability, usability, and adaptability compared to existing network security methods. Visual representations and a comprehensive evaluation further validate the proposed method's effectiveness, positioning it as an advanced and efficient solution for addressing evolving network security challenges.

This research introduces a novel and comprehensive framework for digital forensics-based crime scene interpretation. The proposed framework comprises five algorithms, each serving a distinct purpose in enhancing image quality, extracting features, matching, and constructing a database, recognizing, and reconstructing objects in 3D, and conducting context-aware analysis. An ablation study validates the necessity of each algorithmic step. The framework consistently outperforms existing methods in terms of accuracy, precision, recall, and processing time. A detailed comparative analysis of parameters further highlights its cost-effectiveness, moderate complexity, superior data integration, and scalability. Visualizations underscore its dominance across multiple metrics and parameters, positioning it as an advanced solution for digital forensic-based object recognition in crime scene interpretation.