Journal of Intelligent Systems and Internet of Things

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Energy Efficient Cluster Head Selection Using Hybrid RL-PSO Approach

Arpita Choudhary , N. C. Barwar , Vikas Chouhan

Wireless Sensor Networks (WSNs) are crucial in several applications, highlighting the need of effective clustering and fault detection systems.  This paper introduces a novel approach that uses Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to optimize cluster head selection and enhance fault detection capabilities within WSNs. The proposed hybrid algorithm operates in two phases, combining the explorative capabilities of RL with the optimization process of PSO to select cluster heads based on residual energy and connectivity considerations. By continuously monitoring the network's residual energy state and the number of active nodes, the proposed method ensures prolonged network lifetime and improved overall performance. Our experimental results demonstrate the superior performance of the hybrid RL-PSO approach compared to traditional clustering algorithms, showcasing significant improvements in optimizer accuracy, residual energy preservation, and fault detection efficiency.

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Vol. 11 Issue. 2 PP. 08-21, (2024)

Improving Support vector machine for Imbalanced big data classification

Alaa Abdulazeez Qanbar , Zakariya Yahya Algamal

A significant proportion of one type of pattern and a relatively small quantity of another type of pattern can be found in many unbalanced real data sets. In addition, finding significant observations and excluding influential observations is effectively accomplished through diagnostic analysis. Support vector machines (SVM), a common classification technique, perform poorly on imbalanced datasets and when influential observations exist. In this research, the pigeon optimization algorithm as a metaheuristic algorithm is employed to address the influence observation issues in SVM. Experiments are done on three real sets of data. Our approach provides higher classification accuracy compared to other widely used algorithms. This approach could be used for further biological, chemical, and medical datasets.

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Vol. 11 Issue. 2 PP. 22-29, (2024)

Unraveling the Complexity: A DEMATEL Analysis of the Negative Impact of Artificial Intelligence (AI) Adoption among Students in Higher Education

Zahari Md Rodzi , Wan Normila Mohamad , Zhang Lu , Faisal Al-Sharqi , Rawan A. shlaka , Ashraf Al-Quran , Ali M. Alorsan Bany Awad

This research employs DEMATEL analysis as a methodological approach to thoroughly examine the adverse consequences of implementing Artificial Intelligence (AI) among students enrolled at Universiti Teknologi MARA (UiTM) Negeri Sembilan, Malaysia. The analysis encompasses three distinct professional cohorts: student representatives, academic staff, and upper management. Through a systematic analysis of causal relationships between multiple factors, this study aims to identify and prioritize the fundamental elements contributing to the negative consequences associated with integrating artificial intelligence. The prominence of privacy and security concerns as a causal factor highlights the importance of implementing strong data protection measures and adhering to ethical practices related to AI. Furthermore, various factors connected with personal disconnection, restricted adaptability, dependance on technology, and insufficient emotional intelligence influence the adverse outcomes of artificial intelligence implementation among students. The results underscore the necessity of implementing focused interventions and strategies to tackle these difficulties and guarantee a harmonious and advantageous integration of artificial intelligence in students' educational journeys. Higher education institutions can effectively harness the advantages of AI while ensuring their students' welfare and educational achievements by recognizing and proactively addressing any potential limitations.

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Vol. 11 Issue. 2 PP. 30-41, (2024)

Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index

Bashar Talib AL-Nuaimi , Ruaa Azzah Suhail , Sanaa adnan abbas , El-Sayed M. El-kenawy

Early detection of Lung tumors, which is lethal and equally affects men and women, is challenging. In order to decrease mortality rates and raise survival rates, early detection and classification of Lung tumors is essential. However, at the start of 2020, the entire planet would be afflicted with a coronavirus that causes a fatal sickness (COVID-19). CT imaging is a good tool to detect illness among the various COVID-19 screening techniques available. On the other hand, alternative methods of disease detection take a lot of time. Deep learning, a type of machine learning, opens up a wealth of opportunities for investigating and assessing tumor features using CT scans, allowing for improved disease prediction, diagnosis, and classification. Using CNN, DNN, and VGG-16 models, the suggested approach in this research gives unambiguous and accurate categorization.

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Vol. 11 Issue. 2 PP. 42-51, ()

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Prashant Kumar Shukla , Ratish Agarwal

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

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Vol. 11 Issue. 2 PP. 42-62, (2024)

Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

T. Vivekanandan , J. Jegan , D. Jagadeesan , Y. Sreeraman , N. Ch. S. N. Iyengar , E. Purushotham

Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques.

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Vol. 11 Issue. 2 PP. 63-74, (2024)