IoT based Wireless Networks in Hospitals: Ensuring Seamless Communication in Critical Situations

Madhura .K; Rahul Yadav; Yuvraj Parmar; Tressa Michael; Kiran Sanjay Degan; Prakriti Kapoor

doi:https://doi.org/10.54216/JISIoT.140120

Journal of Cybersecurity and Information Management | 2025

The Challenge of Adversarial Attacks on AI-Driven Cybersecurity Systems

As AI is deployed increasingly in defensive systems, hostile assaults have increased. AI-driven defensive systems are vulnerable to attacks that exploit flaws. This article examines the approaches used to resist AI-based cybersecurity systems and their effects on security. This paper examines existing literature and case studies to demonstrate how attackers modify AI models. These include avoidance, poisoning, and data-driven assaults. It also considers data breaches, system failures, and unauthorized access if a hostile effort succeeds. The report recommends adversarial training, model testing, and input sanitization to address these issues. It also stresses the need for monitoring and updating AI algorithms to adapt to changing opponent tactics. This paper emphasizes the need to limit hostile strike threats using real-life examples and statistics. To defend AI-driven cybersecurity systems from complex threats, cybersecurity specialists, AI researchers, and policymakers must collaborate across domains. This article provides full guidance for cybersecurity and AI professionals. It describes the complex issues adversarial assaults create and proposes a flexible and robust architecture to safeguard AI-driven cybersecurity systems from emerging threats.

groups

M. N. V Kiranbabu mail -

A. Jeraldine Viji mail -

Amit Kumar Chandanan mail -

Vijay Birchha mail -

Tushar Kumar Pandey mail -

Sumit Kumar Sar mail

link https://doi.org/10.54216/JCIM.150123

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Hybrid Temporal Lambda Layer Embedded in Autoencoder Neural Network for Fake News Detection

Many social media applications use different animated or morphed images to make fake news viral. Recognition of text from images for their classification as real or fake requires a neural network. BERT (Bidirectional Encoder Representation Transformer) or MLP-based (Multi-Layer Perceptron) algorithms are successful when working with textual data alone. However, the system needs to extract the sequential text from the images to identify the semantic meaning of the content before the classification process. The dataset utilized was acquired from The Indian Fake News Dataset (IFND) contains text and visual data from 2013 to 2021. The data includes both visual and textual information, as well as 126k data points obtained from millions of users. In the proposed model, a squeezed lambda is implemented to process the data in the three forms of verbal tenses, i.e., past to future and future to past. In the lambda layer, temporal classification is performed by applying two bidirectional LSTM (Long Short Term Memory) layers based on the retuning sequences of the character list available in the dataset. It also computes the batch cost of every iteration and reduces them based on the ratio of prediction and input class labels available. To ensure that the suggested technique is more accurate than the current approach, a validation was undertaken, resulting in a +0.5 increase in accuracy over the BERT (Bidirectional Encoder Representation Transformer) model. Hence, the proposed method has achieved higher accuracy than existing algorithms. Than existing algorithms.

groups

T. V. Divya mail -

Figlu Mohanty mail

link https://doi.org/10.54216/JCIM.150124

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Predictive Modeling of Muscular Performance and Fitness Progression using Artificial Intelligence

This study presents a novel approach to predictive modeling of muscular performance and fitness progression using artificial intelligence techniques. Leveraging advanced machine learning algorithms, including artificial neural networks (ANN), support vector machines (SVM), and gradient boosting machines (GBM), we develop a comprehensive model capable of accurately forecasting key metrics related to muscular strength, endurance, and overall fitness. Extensive experimentation and evaluation demonstrate the superiority of the proposed method over existing algorithms across a range of performance metrics, including accuracy, precision, recall, F1-score, and error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our findings highlight the importance of feature selection techniques and model hyperparameter optimization in driving predictive performance, underscoring the need for careful model development and tuning. The practical implications of our research extend to sports science and athletic training, where the proposed method can inform personalized training strategies tailored to individual athletes' needs and goals. Moving forward, further research is needed to validate the robustness and generalizability of the proposed method across different populations and athletic disciplines, as well as to explore its integration with real-time data sources for more dynamic and responsive training programs.

groups

Manshuralhudlori mail -

Agus Kristiyanto mail -

Rony Syaifullah mail -

Febriani Fajar Ekawati mail -

Slamet Riyadi mail -

Fadilah Umar mail

link https://doi.org/10.54216/FPA.170113

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Enhancing Financial Fraud Detection using Temporal Patter Mining Technique

Examining the temporal behavior of common patterns, obtaining appropriate clusters, and reducing the size of discovered patterns are three significant challenges in temporal data mining. Among the available methods, the constraint-based pattern mining approach has achieved remarkable progress in this domain. Apriori and Interleaved algorithms, which are both slow and outdated, are nonetheless used by present time-granularity pattern exploration approaches. To address these issues, we propose the Frequent Pattern Growth method with Special Constraints. The system incorporates a method for generating patterns on a regular basis. It mandates that transactional datasets adhere to complete and partial cyclic criteria. To locate all possible periodic patterns within the Spatio temporal database, we redefine the task as periodic pattern mining in this thesis. The proposed method makes use of a periodic pattern tree miner. To begin, the clustering method uses an innovative global pollination artificial fish swarm technique to create the most effective dense clusters.

groups

Ahmed Aziz mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJAACI.060206

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Fuzzy Approach for Congestion Avoidance in FANET and IoT

In the recent era of communication technology, flying ad hoc networks are gaining popularity because of their flexibility and broad area of application to gather data from environmental sources with limited infrastructure. FANET nodes, or unmanned aerial vehicles (UAVs), are heterogeneous devices, and coordination between the UAVs is an important part of communication with limited battery power sources. In ad hoc networks, devices have limited battery power, so proper battery utilization is critical to maintaining network connectivity. In order to establish a network without congestion, it is vital to have inter-UAV and IoT wireless communication for cooperation and collaboration among many UAVs. UAV connections may experience frequent disconnections. Another obstacle is the limited distance allowed between the stations. The routing algorithm selects only the nodes that are specifically requested by the source node based on its requirements and maintains the source node no longer needs the route until it. IoT devices have limited processing capability and memory. A single mobile device controls the IoT devices, or users can use the concept of automation to control the functioning of smart IoT devices. This research proposes a fuzzy-based congestion control scheme (MCPFB) to control the congestion between UAVs and IoT devices. UAVs are faster, and IoT devices can collect information from UAVs and forward it to other devices. The UAV’s can store limited and sufficient types of information, but during routing, only a single path is available, which causes congestion in the FANET-IoT network. The fuzzy based load prediction and balancing routing is able to handle the problem of congestion in FANET-IoT. In order to overcome the problem of congestion with improper energy utilisation, this paper presents fuzzy rule-based congestion control techniques for a flying ad hoc network. We focus on the efforts to reduce congestion in the FANET-IoT network. Routing is a critical issue in FANET-IoT and hence the focus of this research is on the performance improvement of routing in FANET-IoT. Packets dropping on the nodes show congestion occurrence in the network, and the possibility of lost connectivity with other nodes is high. Unlike the aforementioned works, the proposed MCPFB routing shows better performance compared to the conventional BARS scheme in FANET-IoT.

groups

Mahendra Sahare mail -

Priti Maheshwary mail -

Vinay Kumar Dwivedi mail

link https://doi.org/10.54216/JISIoT.140115

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoTBlockFin: A Solution to Prevent Loan Scams in India with Integrating IoT and Blockchain for Enhanced Security and Transparency in Loan Processing

Loan frauds in India have gotten more difficult by exploiting financial system vulnerabilities. Online purchasing has exacerbated these frauds. Identity fraud, phoney paperwork, and unclear loan conditions are common. This article looks at how blockchain and IoT could make loans safer, more open, and more efficient, reducing loan fraud. On an independent blockchain network, the proposed IoTBlockFin system records all loan events. This opens up the system and prevents dishonest alterations. IoT devices verify borrower identities and property, reducing false claims. An online loan application and smartphone app allow remote loan status checks. This speeds up and simplifies client service. Blockchain's digital safety measures protect sensitive user and transaction data from unauthorised parties. This prevents data breaches and illegal access. This comprehensive approach reduces loan frauds and improves financial transactions. IoTBlockFin seeks to solve today's lending process, which will transform India's banking business.

groups

Akhtar Hasan Jamal Khan mail -

Syed Afzal Ahmad mail

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

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Efficient Routing and Lifetime Prolongation in IoT founded Wireless Sensor Network Performance with Bee Colony-Inspired Lifetime Enhancement

To extend the lifespan of Wireless Sensor Networks (WSNs), effective routing protocols are required to provide communication channels between the sources and sink. While nodes are arbitrarily distributed in a substantially unsafe situation, these steering protocols are susceptible to an extensive range of assaults. For WSNs, trust-based routing protocols are created, which employ a trusted route rather than the quickest path, to prevent these attacks. The artificial bee colony-based clustering technique is utilized because the conventional clustering algorithm reduces the energy usage of nodes. This allows it to increase the lifespan of the sensor network by evenly dividing energy use among all nodes. The artificial bee colony (ABC)-based grouping method was developed because the typical grouping technique minimizes the energy usage of nodes. By integrating diverse sensors and devices, Internet of Things (IoT) enhances the performance of WSN, by enabling efficient data collection, analysis, and communication. The creation of such traditional protocols does not guarantee the best global optimization for the lengthening of WSN life. Through simulation analysis, the suggested Artificial Bee Algorithm (ABC)-based Traffic-Aware Energy Efficient Routing (TEER) protocol's performance was evaluated and contrasted with the TEER protocols. The ABC-based TEER protocol's lifetime analysis, active node analysis is achieved and contrasted with those of other protocols. In terms of the number of rounds, the network performance for the ABC-based TEER scheme performs better than the TEER schemes. The Analysis of throughput of the ABC-TEER method, which reveals a 9.5% increase in performance in comparison to the TEER protocol.

groups

Megha Gupta mail -

Sunil Kr Pandey mail -

Piyush Kumar Pareek mail -

Prashant Kumar Shukla mail -

Puneet Kumar Aggarwal mail -

P. Venkateswarlu Reddy mail

link https://doi.org/10.54216/JISIoT.140117

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

The Future of Personalized Medicine and Internet of Things Reshaping Healthcare Treatment Plans and Patient Experiences

The article "The Future of Personalized Medicine and How the Healthcare Internet of Things is Reshaping Treatment Plans and Patient Experiences" offers a comprehensive exploration of the transformative landscape of healthcare. The introduction highlights the paradigm shift from a generalized approach to personalized medicine, where treatments are tailored to individual genetic and lifestyle profiles. Leveraging advanced data analytics and the Healthcare Internet of Things (IoT), the study investigates the impact of these technologies on treatment plans and patient experiences. Employing a multifaceted approach, the research integrates various methods, including logistic regression, random forest, support vector machines, neural networks, and time series analysis, to assess their efficacy in reshaping healthcare practices. Evaluation metrics, such as accuracy, sensitivity, specificity, F1 score, computational cost, and data security, are employed to compare the proposed method with traditional approaches, revealing the superiority of the proposed method across multiple parameters. The results demonstrate the transformative potential of personalized medicine and the Healthcare IoT in enhancing healthcare outcomes and patient experiences. For instance, the proposed method achieves an accuracy of 95%, significantly surpassing traditional methods that average around 89%. Sensitivity, a critical metric in healthcare, reaches 92%, demonstrating the proposed method's ability to identify true positives with higher precision. Additionally, the computational cost of the proposed method, at 0.015, is notably more efficient than traditional methods, which range from 0.020 to 0.022. These numerical values underscore the superior performance of the proposed method, highlighting the importance of integrating cutting-edge technologies for optimized patient care. In conclusion, the study underscores the imperative of embracing a patient-centric approach in healthcare.

groups

Tan lan Hong mail -

Yagnik Dave mail -

Ankur Khant mail -

Lokesh Verma mail -

Megha Chauhan mail -

S. Parthasarathy mail

link https://doi.org/10.54216/JISIoT.140118

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

The Integration and Implementation of the Healthcare Internet of Things and Its Comprehensive Analysis

The Healthcare Internet of Things (HIoT) is driving a paradigm shift in the healthcare business by providing safe, fast, and networked healthcare solutions. We examined the advantages, disadvantages, and potential future of the Internet of Things (IoT) in the medical industry. Scalability, accuracy, real-time monitoring, data security, and interoperability were among the top priorities. The study employed strict assessment criteria to compare the proposed HIoT technology to existing approaches. This article begins with an overview of the IoT in healthcare. This study compares and contrasts the proposed HIoT strategy with more conventional approaches. We applied both methodologies in this study, each with its own benefits and drawbacks. We evaluated the responses using the F1-score, recall, accuracy, and precision. The inquiry uncovered an interesting story. The proposed HIoT method outperformed traditional techniques in all assessment parameters. In terms of accuracy, the recommended solution outperformed "Block chain Encryption" (8.4) and "Data Validation" (7.9). Additionally, it received an 8.9 for real-time monitoring and an 8.8 for interoperability. Another benefit of the strategy was a reduction in medical errors. The high data accuracy score of 9.1 demonstrates this. The findings illustrate the potential transformation of healthcare delivery through the Internet of Things. According to the study, the proposed strategy might increase healthcare's efficacy, efficiency, and patient-centeredness. The Internet of Things has opened up exciting new opportunities in healthcare. These options may transform medical care and patient outcomes.

groups

Adilakshmamma .T mail -

Meharunnisa S. P. mail -

Anusha Sreeram mail -

Rajat Saini mail -

Maryanka mail -

Shikhar Gupta mail

link https://doi.org/10.54216/JISIoT.140119

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoT based Wireless Networks in Hospitals: Ensuring Seamless Communication in Critical Situations

The heading "Wireless Networks in Hospitals: Ensuring Seamless Communication in Critical Situations" examines hospital wireless network enhancement. When patient well-being is at stake, this strategy encourages honest conversation. Service quality, resource efficiency, and network security are crucial. These mathematical models increase hospital wireless network stability based on Internet of Thing (IoT). Service management effectiveness influences who gets vital medical information quickly. Information and crucial messages are delivered faster. A mathematical technique considers the relevance and transmission time of each data payload to estimate its priority factor (P(i)). Network performance determines QoS settings. Priority data is transmitted first to ensure quick delivery to the intended recipients. This technology is essential for updating hospital WiFi networks, especially in critical situations where it can transmit accurate and timely information and save lives. WiFi reliability is essential for building operations. Compare failure frequency and MTBF to assess each network point's reliability. An exponential reliability function determines network dependability. The mean time between failures is used. This method maintains network functionality despite its complexity. Determine which pieces are crucial and how they influences network health. This simplifies network backups and maintenance. Load balancing distributes network tasks among entry points. This strategy helps the network function smoothly and minimize congestion during peak demand. The weighted round-robin timing algorithm determines how busy each access point is to send fresh network traffic to the proper areas. By equally distributing load and prioritizing underutilized access points, this method maintains network stability and keeps critical lines available. These three approaches form a full healthcare WiFi network strengthening plan. Mission-critical data is prioritized, the network is more robust, and resources may be allocated quickly. Our solution often outperforms the existing standard in network stability, communication, and cost.

groups

Madhura .K mail -

Rahul Yadav mail -

Yuvraj Parmar mail -

Tressa Michael mail -

Kiran Sanjay Degan mail -

Prakriti Kapoor mail

link https://doi.org/10.54216/JISIoT.140120

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new