On Some Topological Spaces Based On Symbolic n-Plithogenic Intervals

Raed Hatamleh; Ayman Hazaymeh

doi:https://doi.org/10.54216/IJNS.250102

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

Details open_in_new

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

Details open_in_new

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

Details open_in_new

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

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Advances in Wearable Sensors for Real-Time Internet of things based Biomechanical Analysis in High-Performance Sports

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.

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Vilas Alagdeve mail -

Ranjan K. Pradhan mail -

R. Manikandan mail -

P. Sivaraman mail -

Sarihaddu Kavitha mail -

Shaeen Kalathil mail

link https://doi.org/10.54216/JISIoT.130209

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.

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Saurabh Sharma mail -

Ghanshyam Prasad Dubey mail -

Harish Kumar Shakya mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.160204

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Employing Deep Learning Techniques for the Identification and Assessment of Skin Cancer

These days, skin cancer is a prominent cause of death for people. Skin cancer is the name given to the abnormal development of skin cells that are exposed to the sun. These skin cells can develop anywhere on the human body. The majority of malignancies are treatable in the early stages. Thus, early detection of skin cancer is anticipated in order to preserve patient life. With cutting edge innovation, it is possible to detect skin cancer early on. Here, we provide a novel framework for the recognition of dermo duplication pictures that makes use of a neighbouring descriptor encoding method and deep learning technique. Specifically, the deep representations of a rescaled dermo duplication image that were initially removed through training an extraordinarily deep residual neural network on a big dataset of normal images. Subsequently, the neighbourhood deep descriptors are obtained by request-less visual measurement highlights, which rely on fisher vector encoding to create an international image representation. Lastly, a convolution neural network (CNN) was utilised to orchestrate melanoma images employing the Fisher vector encoded depictions. This proposed technique can give more discriminative parts to oversee huge contrasts inside melanoma classes and little varieties among melanoma and non-melanoma classes with least readiness information.

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Sowmya Koneru mail -

Pappula Madhavi mail -

Krishna Kishore Thota mail -

Janjhyam V. Naga Ramesh mail -

Venkata Nagaraju Thatha mail -

S. Phani Praveen mail

link https://doi.org/10.54216/FPA.160205

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

A Novel Secured Deep Learning Model for COVID Detection Using Chest X-Rays

Automatic detection of a medical disease is a need of the hour as it helps doctors diagnose diseases and provide fast medical reports. COVID-19 is a deadly disease for which an automated detection system may be helpful. This study proposes a unique hybrid deep learning model, COVIDet, based on CNN and the speeded-up robust features (SURF) extraction approach to diagnose COVID-19 using chest x-ray images. SURF is utilized in this work to extract features, and the output is then transferred to a 25-layer CNN for detection using the extracted features. This investigation employed 4623 COVID-19 positive X-ray pictures or 8055 total. The suggested hybrid model also contrasts with the study's VGG19, Resnet50, Inception, Xception, and traditional CNN models. The proposed model had a 98.01% accuracy, a 97.03% F1-score, a 98.65% sensitivity, a 99% precision, and a 95.65% specificity. The proposed model can be further improved when more datasets are available and can help doctors to diagnose patients quickly and efficiently. Using chest X-ray pictures, a secured web application is also developed to identify COVID-19. The user sends the application a chest X-ray image, and in return, it determines whether an individual is COVID-19 positive or not, cutting down on testing time. In Covid times, when people are standing in long queues and waiting for their turns, this application would greatly help. The application uses the pre-trained COVIDet model in the backend.

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Chhaya Gupta mail -

Vasima Khan mail -

Ramya Srikanteswara mail -

Nasib Singh Gill mail -

Preeti Gulia mail -

Sindhu Menon mail

link https://doi.org/10.54216/JCIM.140116

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

An Integrated DEMATEL with Bipolar neutrsophic Dombi-based Heronian Mean Operator and Its Applications in Decision-making Problem

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is commonly used in examining and illustrating the relationship between different factors in a complex system. This paper proposes a novel approach that integrates the Bipolar neutrosophic Dombi-based IGWHM operator into the DEMATEL method, in which the criteria are analyzed by means of the cause-and-effect relationship diagram. The current studies on the classical DEMATEL approach have some limitations on the aggregation process, particularly in capturing the interrelationship of individual arguments by assessing their impact on each other within a complex system. To enhance the aggregation of complex information in the decision-making framework, the Bipolar neutrosophic Dombi-based Improved Generalized Weighted Heronian mean (IGWHM) operators are employed. The applicability and effectiveness of the proposed approach are demonstrated when solving a selection of transport service providers. The ability of the method to highlight the intricate interdependencies and ranking criteria based on their importance. The sensitivity of the developed approach is observed with variations in the involved parameter. Moreover, a comparative analysis is made with other methods to demonstrate its validity.  

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Siti Nurhidayah Yaacob mail -

Hazwani Hashim mail -

Nor Hashimah Sulaiman mail -

Noor Azzah Awang mail -

Ashraf Al-Quran mail -

Lazim Abdullah mail

link https://doi.org/10.54216/IJNS.250101

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025