Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment

Jameela Ali; Sulabh; A. Mohamed; Sudhanshu; Akshay Kumar; Jaymeel

doi:https://doi.org/10.54216/JISIoT.130208

International Journal of Neutrosophic Science | 2024

Selection process based on new type neutrosophic interval-valued set applied to logarithm operator

We introduce the new type neutrosophic interval-valued set (NIVS) problems relevant to multiple attribute decision making (MADM). Pythagorean interval-valued fuzzy set (PIVFS) and neutrosophic set (NS) can be extended into new type neutrosophic interval-valued set. We discusses new type neutrosophic interval-valued weighted averaging (new type NIVWA), new type neutrosophic interval-valued weighted geometric (new type NIVWG), generalized new type neutrosophic interval-valued weighted averaging (new type GNIVWA) and generalized new type neutrosophic interval-valued weighted geometric (new type GNIVWG). A number of algebraic properties of new type NIVSs have been established such as associativity, distributivity and idempotency. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.  

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Lejo J. Manavalan mail -

Sadeq Damrah mail -

Ibraheem Abu Falahah mail -

Abdallah Al-Husban mail -

M. Palanikumar mail

link https://doi.org/10.54216/IJNS.240424

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Vol. Volume 24 / Iss. Issue 4

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Pure Mathematics for Theoretical Computer Science | 2024

Incorporating Kernels into Convolutional Neural Networks for Enhanced Feature Extraction

The simulation was used to evaluate the method of kernel K , Neural network NN, Convolution Neural network  CNN bys using (MINIST) data set. The accuracy of the method was tested and compared with the convolutional neural network as well as with the kernel function for the same input data (training and testing). The results of simulation showed that there is a high accuracy of the method, and at the same time there is a decreasing loss over the epochs, which indicates the. We note high smooth by method for recognize among features.  

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Sackineh Shamil Jasim mail

link https://doi.org/10.54216/PMTCS.040102

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Vol. Volume 4 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Digital Forensic Based Object Recognition for Enhanced Crime Scene Interpretation

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.

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Vikash Kumar Singh mail -

Durga Sivashankar mail -

Siddharth Sriram mail -

Manish Nagpal mail -

Warish Patel mail -

Shweta Loonkar mail

link https://doi.org/10.54216/JISIoT.130201

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Comprehensive Analysis of Implementation and Evaluation IoT based Techniques in Networked Security Systems

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.

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Raenu Kolandaisamy mail -

Suhas Gupta mail -

Shashikant Patil mail -

Jaymeel Shah mail -

Abhinav Mishra mail -

N. Gobi mail

link https://doi.org/10.54216/JISIoT.130203

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering

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%.  

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Ahmed Saeed Alabed mail -

Rajesh Kumar Samala mail -

Asha KS mail -

Sorabh Sharma mail -

Amit barve mail -

Deepak Minhas mail

link https://doi.org/10.54216/JISIoT.130204

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Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Emerging Trends: Nano-Scale Wireless Sensor Networks and Applications

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.

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Julissa E. Reyna-Gonzalez DRA mail -

N. K. Rayaguru mail -

Gowrishankar J. mail -

Bhargavi Gaurav Deshpande mail -

Madhur Grover mail -

Daxa Vekariya mail

link https://doi.org/10.54216/JISIoT.130202

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Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Intelligent Integration of Wearable Sensors and Artificial Intelligence for Real-time Athletic Performance Enhancement

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.

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Prabhat Kr. Srivastava mail -

Ram Kinkar Pandey mail -

Gaurav Kumar Srivastava mail -

Nishant Anand mail -

Kunchanapalli Rama Krishna mail -

Prateek Singhal mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.130205

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Optimized LoRaWAN Architectures: Enhancing Energy Efficiency and Long-Range Connectivity in IoT Networks for Sustainable, Low-Power Solutions and Future Integrations with Edge Computing and 5G

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.

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Nishant Anand mail -

Pritee Parwekar mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.130206

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

An Operative IoT Grounded AEEBLR (Ant-Founded Efficient Energy and Balanced Load Routing) Method for Path Conjunction in Mobile Ad Hoc Networks Approach

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.

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Safaa H. OBAIDI al-Khafaji mail -

Julissa E. Reyna-Gonzalez DRA mail -

Sukhman Ghumman mail -

Hannah Jessie Rani R. mail -

Raj Kumar mail -

Shikhar Gupta mail

link https://doi.org/10.54216/JISIoT.130207

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Artificial Intelligence-Enabled Muscular Movement Analysis in Wireless Body Area Networks for IoT based Fitness Assessment

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.

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Jameela Ali Alkrimi mail -

Sulabh Mahajan mail -

A. Mohamed Jaffer mail -

Sudhanshu Dev mail -

Akshay Kumar V. mail -

Jaymeel Shah mail

link https://doi.org/10.54216/JISIoT.130208

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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