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Journal of Intelligent Systems and Internet of Things | 2025

TRIP-CID: Transformer and ResNet Improved Pest Classification and Identification Detection Model for Pesticide Management in Precision Agriculture

In these modern agriculture system crop pests causes major social, economic and environmental issues worldwide. Each pest necessitates an alternative method of control and precise detection has become a very important challenge in agriculture. Deep learning technique shows remarkable results in image identification. Standard pest detection framework might struggle with accuracy due to complicated algorithms and lack of data, and result in incorrect detection, which leads to harm the crop environment. To end this, we developed a novel framework named Transformer and ResNet Improved Pest Classification and Identification Detection (TRIP-CID) for crop pest classification and identification. At first, the pest images are obtained through the benchmark dataset for pre-processing. The Pre-processed images are immediately delivered to the Improved ResNet (IR-Net) and Pyramidal Vision Transformer (PVT) for multi-scale spatial, channel and contextual feature maps extraction within three stages. The extraction feature maps in the two modules are combined to produce a superior feature map. Then refined feature maps was fed to the three distinct Machine Learning (ML) classifiers offered pest detection outcomes. For accurate results, we employ ensemble-voting technique, which outputs effective pest detection result that is vastly used for particle suggestion. Finally, we utilized presented technique for detecting and identify crop pest in 10-pest class for instance larva of laspeyresia pomonella, Euproctis pseudoconspersa strand, Locusta migratoria, acrida cinerea, empoasca flavescens, spodoptera exigue, parasa lepida, chrysochus chinensi, L.pomonella types of insects pests and larva of S. exigua. Additionally, the suggested methodology has shown to provide experts and farmers with quick, efficient assistance in identifying pests, saving money and preventing losses in agricultural output.

groups
R. Kiruthika mail -
B. Arun kumar mail
link https://doi.org/10.54216/JISIoT.170103

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models

This work explores the innovative application of integrated pest management (IPM) strategies in the control of the Tea Looper Caterpillar and the Tea Leaf Hopper, utilizing the YOLO algorithm for real time pest detection. IPM is essential for sustainable agriculture, aiming to reduce chemical pesticide usage through a combination of biological, cultural, and technological methods. The combination of artificial intelligence and machine learning into IPM practices has shown promising results, particularly in identifying and monitoring pest populations in tea plantations. This study reviews existing literature on the impact of various pests on tea crops and highlights the significance of using advanced algorithms for effective pest management. Notably, the implementation of the YOLO algorithm demonstrated an impressive accuracy rate of 97% in detecting these pests, displaying its potential to enhance pest control efforts. By focusing on the tea green leafhopper and looper caterpillars, the research aims to provide insights into sustainable pest control methods that minimize environmental impact. The findings underscore the potential of AI-driven technologies in enhancing agricultural productivity while promoting ecological balance. This project ultimately contributes to the ongoing discourse on sustainable agricultural practices and the role of technology in addressing pest-related challenges in tea cultivation.

groups
Malathi K. mail -
Mohanasundaram N. mail -
Santhosh R. mail -
Manikandan B. mail -
Parthasarathy V. mail -
Saravana Kumar G. mail
link https://doi.org/10.54216/JISIoT.170104

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Park-Net: Multi-modality qEEG and fMRI-based PDDiagnosis via Attentional Graph Convolutional Neural Network

Parkinson's disease (PD) is a degenerative neurological condition instigated by the death of dopamine-producing neurons in the brain, which is manifested as tremors, rigidity, bradykinesia, and postural instability. Early and accurate diagnosis of PD is crucial for timely initiation of appropriate treatment strategies, which can help alleviate symptoms, advance excellence of life, and hypothetically leisurely disease development. A promising method for PD diagnosis is the combination of fMRI and qEEG methods, which provide full neuroimaging data to improve accuracy and early detection. However, recent studies are limited in performing and achieving accurate PD diagnosis. To alleviate this issue, we have proposed graph neural network-based PD diagnosis model addressed as Park-Net. Here, data pre-treatment is initially implemented in which both collected qEEG signal and fMRI image is denoised using Discrete Wavelet Transform (DWT) and Improved Kalman Filter (IKF) respectively. Following that, appropriate region of fMRI is segmented by adversarial network-based U-Net (AN-Net). After that, segmented region is fed into proposed Park-Net model; here modality encoder (ME) encompassed Long Short-Term Memory (LSTM) for feature extraction. We adapted Multi-modal Fused Attentional Graph Convolutional Neural Network (MAGCN) for constructing graph based on feature correlation and then fused. Finally, we designed Self-Attention Pooling with softmax layer for classifying PD as normal or abnormal. We have implemented our proposed Park-Net model to evaluate model performance, and its efficacy is assessed using a range of performance metrics such as accuracy, sensitivity, specificity, F1-Score, and ROC curve, highlighting its superior performance compared to existing methods in PD diagnosis approaches.

groups
S. Mohanapriya mail -
Kamalraj Subramaniam mail
link https://doi.org/10.54216/JISIoT.170105

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Minimizing Time Overhead in VANET Task Offloading: A Novel Preparatory-Based Edge-Cloud Collaborative Model

In vehicle ad hoc networks (VANETs), vehicles often need to perform complex computing tasks that may exceed their processing capabilities within the required period to provide enhanced services. A common approach to improving service performance is to offload tasks to roadside units (RSUs). However, RSUs might not always have sufficient resources to manage all task assignments effectively. Given the increasing processing power of modern vehicles, task delegation to other vehicles presents a viable alternative to relying solely on RSUs. To achieve this, we first introduce a probabilistic approach that relaxes discrete actions, such as cloud server selection, into a continuous space. We then implement a Supportive Multi-Agent Deep Reinforcement Learning (SMADRL) technique that minimizes total system costs, including Vehicle device energy consumption and cloud server rental charges, by utilizing a centralized training and distributed execution approach. In this framework, each Vehicle device operates as an independent agent, learning efficient decentralized policies that reduce computing pressure on the devices. Experimental results show that the proposed SMADRL framework effectively learns dynamic offloading policies for each Vehicle device and notably outperforms four state-of-the-art DRL-based agents and two heuristic frameworks, resulting in reduce overall system costs.

groups
K. Rajeswari mail -
B. Arun kumar mail
link https://doi.org/10.54216/JISIoT.170106

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

A Deep Learning-Based Guidance for Stuttering Prediction

Advanced stuttering detection and classification using artificial intelligence is the main emphasis of this work. Determining the degree of stuttering for speech therapists, providing an early patient diagnosis and facilitating communication with voice assistants are just a few of the uses for an efficient classification of stuttering and its subclasses. This work's first portion examines the databases and features utilized, along with the deep learning and classical methods used for automated stuttering categorization. The Bayesian Bi-directional Long Short Memory with Fully Convoluted Classifier model (BaBi-LSTM) is a deep learning model in conjunction with an available stuttering information set. The tests evaluate the impact of individual signal features on the classification outcomes, including pitch-determining variables, different 2D speech representations, and Mel-Frequency Cepstral Coefficients (MFCCs). The suggested technique turns out to be the most successful, obtaining a 95% F1 measure for the entire class. When detecting stuttering disorders, deep learning algorithms outperform classical methods. However, the results differ amongst stuttering subtypes because of incomplete data and poor annotation quality. The study also examines the impact of the number of thick layers, the magnitude of the training information set, and the division apportionment of data into training and evaluation groups on the effectiveness of stuttering event recognition to offer insights for future technique improvements.

groups
Rajeswary Nair mail -
K. S. Kannan mail
link https://doi.org/10.54216/JISIoT.170107

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Pure Mathematics for Theoretical Computer Science | 2025

LS-Extending Fuzzy Modules

The main aim of this paper is extend the notion of S-extending fz-modules into LS-extending fz-modules and study this new notion. This lead us introduce and study other notions such as: purely semisimple, purely extending and purely y-extending fz-modules. Moreover, the relationships LS-extending fz-module with the various types.

groups
Hassan K. Marhon mail
link https://doi.org/10.54216/PMTCS.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2025

A New class Closed Sets in Fuzzy Neutrosophic Topology

The goal of this study is to introduce fuzzy neutrosophic -closed sets, a novel notion of collections in fuzzy neutrosophic topology. In this research, we use certain novel concepts, theories, and hypotheses to explore and analyze other innovative characteristics of these classes. In order to make clear the connections among the new research of -closed sets and other sets, a collection of instances is given and explored.

groups
Yaseen S. R. mail -
Asmaa Ghasoob Raoof mail -
Shadia Majeed noori mail
link https://doi.org/10.54216/IJNS.260311

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

Enhanced Image Encryption through Combined Arnold and Three Other Chaos Techniques

In an era where digital technologies dominate all aspects of life, image encryption has emerged as a fundamental pillar of data protection and securing sensitive information. With the rise of sophisticated cyber threats and attacks, the search for innovative and stronger encryption methods has become an urgent necessity. This research proposes an enhanced image encryption scheme combining the Arnold map, 2D Henon map, memristor elements, and exponential nonlinearity chaos techniques to address vulnerabilities in conventional encryption methods. The hybrid approach ensures robustness against statistical, differential, and brute-force attacks. Experimental results demonstrate superior performance with unified histogram distribution, including near-ideal information entropy (7.99941), infinite peak signal-to-noise ratio (PSNR), and high resistance to differential attacks (NPCR = 99.61%, UACI = 35.08%). A keyspace of  and key sensitivity correlation difference rate (CDR) of 99.61% further validate security. Comparative analysis with recent studies confirms the proposed method’s superiority in encryption strength and computational performance. Consequently, the results of the proposed method making it a promising option for high-security image protection applications.

groups
Sameeh Abdulghafour Jassim mail -
Alaa Abulqahar Jihad mail -
Mohammed I. Khalaf mail
link https://doi.org/10.54216/JCIM.160206

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

An Integrated Cryptographic Approach Using Elliptic Curve Cryptography, Triple Data Encryption Standard and Hash-based Message Authentication Code

Security of digital communication becomes of prime importance due to the fast growing cybersecurity attacks. Classical encryption algorithms frequently drop down in offering the vital level of security required to safeguard critical information. The advances in cryptography methods are very important to solve this issue and ensure integrity and privacy.  This paper focuses on the weaknesses of the current methods through investigating mixing multiple encryption methods. The research explores whether combining Hash-based Message Authentication Code (HMAC), Elliptic Curve Cryptography (ECC), and Triple Data Encryption Standard (3DES) can provide upgrade to security for end-to-end encryption.  The chief objective is to improve and evaluate a powerful encryption framework that make use the strengths of HMAC, ECC and 3DES. This is done by showing how mixing these algorithms together can improve security and reliability levels to safeguard digital communications. An extensive analysis is performed by using several metrics. These involve ciphering and deciphering speed, key generation, NIST test and Avalanche effect. The results show that these combinations increase significantly security level of digital communication. It shows better performance than traditional cryptography in both security and speed. Combining HMAC, ECC and 3DES provide practical solution to increase security level in end-to- end encryption. It improves the vulnerabilities in traditional cryptography by building multi-layer security framework. It is concluded that the proposed framework is powerful and a candidate for developing and has strong resistance against cyber threats.

groups
Farah Tawfiq Abdul Hussien mail -
Sura khalid salsal mail
link https://doi.org/10.54216/JCIM.160207

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new