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

R.; B. Arun

doi:https://doi.org/10.54216/JISIoT.170103

International Journal of Neutrosophic Science | 2025

Application of Neutrosophic Pentagonal Controlled Metric Space via Orthogonality in Traffic Flow Network Using Integral Equation

In this paper, we researched and confirmed some of the axioms of NOPCMS (Neutrosophic orthogonal pentagonal controlled metric space). We used NOPCMS to translate the Banach contraction principle in the formerly defined spaces. Several cases were numerically evaluated, and certain findings were supported, in or- der to review what we found. Furthermore, by demonstrating their existence with a unique and comprehensive solution, we deliver proof of usage and implementation.

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M. Rathivel mail -

M. Jeyaraman mail -

Rahul Shukla mail

link https://doi.org/10.54216/IJNS.260308

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2025

MADM-Strategy using Grey Relational Analysis under Rough Single-Valued Pentapartitioned Neutrosophic Set Environment

This paper aims to introduce various operations in the context of the Rough Single-Valued Pentapartitioned Neutrosophic Set (RSVPNS) environment. Then, based on Grey Relational Analysis (GRA), we propose a Multi-Attribute Decision-Making (MADM) technique. Additionally, we present a practical numerical example to validate the proposed MADM technique in the context of selecting a tourist place for government initiatives aimed at enhancing its attractiveness to tourists.

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Suman Das mail -

Rakhal Das mail -

Prasanna Poojary mail -

Surapati Pramanik mail -

Vadiraja Bhatta G. R. mail

link https://doi.org/10.54216/IJNS.260309

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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Journal of Cybersecurity and Information Management | 2025

Performance Comparison of Wavelet Transforms based Medical Image Compression

Medical image analysis plays a vital role in diagnosis of diseases and the need of the day is to arrive at a simple and efficient compression technique. This paper proposes a comparative analysis of three different wavelet based medical image compression techniques. First algorithm is based on Bi-orthogonal wavelet with Parallel coding  (BiWT-PC) , second is based on Haar wavelet with block coding  (HWT-BC) and third algorithm is based on stationary wavelet transform with Parallel coding (SWT-PC). In this work, 3D medical image is converted into 2D slices and preprocessed using lifting scheme. Wavelet transform is applied to this preprocessed image, which divides the image into multilevel sub-bands. Then, the suitable encoding method is applied to get the compressed image. At the receiver side, the original image is recovered back by applying inverse wavelet transform and proper decoding over the compressed image. Experimentations are carried out over MRI and CT images with four quantitative metrics such as PSNR, CR, DcT and EcT. From the experimental analysis, it is observed that SWT-PC method is quite efficient since it has high PSNR and low CR.

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V. Anusuya mail -

Stency V. S. mail -

G. Srividhya mail -

M. K. Mohammed Faizel mail -

G. Arul Kumaran mail -

R. Santhosh mail -

P. Sherubha mail

link https://doi.org/10.54216/JCIM.160201

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2025

Enhanced Intrusion Detection Using AI-Driven Data Balancing and VQ-VAE-Based Feature Extraction

Network security faces significant challenges due to the increasing sophistication of cyber threats and the inherent class imbalance in intrusion detection datasets. To address this issue, a hybrid Boundary Equilibrium Generative Adversarial Network (BEGFAN) and Vector Quantization Variational Autoencoder (VQVAE) framework, termed BVQVAE, is proposed for Network Intrusion Detection Systems (NIDS). The framework involves preprocessing, feature extraction, and class balancing to enhance classification accuracy. Missing values are imputed, categorical features are label-encoded, and numerical attributes are normalized to ensure a structured dataset. BEGAN generates synthetic samples to mitigate class imbalance, while VQVAE extracts essential features using an encoder with quantization and a decoder for network traffic reconstruction. The model is evaluated on NSL-KDD and UNSW-NB15 datasets, achieving 82.56% accuracy, with precision, recall, G-mean, and F1-score of 86.53%, 87.65%, 86.21%, and 87.08%, respectively.

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Shivanthana S. mail -

Manicka Raja M. mail -

Lalitha Krishnasamy mail -

Karthik R. mail -

R. Venkatesan mail

link https://doi.org/10.54216/JCIM.160202

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2025

PrivaNet-FL: Enhancing Privacy and Minimizing Energy Overhead for Federated Learning System on Edge Devices

In recent years, federated learning (FL) has emerged as a decentralized approach to model training, enhancing data privacy by retaining data on local edge devices. While existing privacy-preserving FL frameworks, like Secure Aggregation and Homomorphic Encryption, protect data through encrypted aggregation, they often face challenges with high communication overhead, significant computational demands, and increased energy consumption. Differential privacy approaches, though customizable via privacy budgets, may also degrade model accuracy due to added noise. Addressing these limitations, we propose PrivaNet-FL (Privacy-Optimized Network for Federated Learning), an advanced FL model that optimizes privacy techniques with minimal energy costs in edge environments. PrivaNet-FL incorporates adaptive privacy and efficiency management across edge devices, such as IoT sensors and smartphones, where data processing and real-time privacy adjustments conserve energy while maintaining data security. The framework consists of three main workflows: (1) Adaptive Privacy-Scaling-modulating privacy based on device constraints, ensuring optimal energy usage through dynamic adjustments of noise in differential privacy or encryption complexity; (2) Lightweight Encryption and Secure Aggregation-employing low-complexity encryption and secure aggregation techniques, such as random masking and distributed averaging, to minimize energy without compromising data privacy; and (3) Energy-Aware Communication-Efficient FL-leveraging model compression, energy-aware scheduling, and differential privacy with controlled noise to reduce communication and energy overhead. Results demonstrate that PrivaNet-FL achieves superior model accuracy with reduced energy and communication costs compared to traditional FL methods, making it ideal for privacy-sensitive and resource-limited edge applications.

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D. Gowthami mail -

M. Vigenesh mail

link https://doi.org/10.54216/JCIM.160203

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2025

MTCM: Refining Privacy-Aware Task Offloading with HGSA in Multi-Tier Computing System for Emerging Next-Generation Wireless Networks -Based Predictor

Multi-cloud computing is emerging as a transformative solution to meet the extensive computational demands of Internet of Things (IoT) devices. In networks with multiple devices and clouds, factors such as real-time computing requirements, fluctuating wireless channel conditions, and dynamic network scales introduce significant complexity. Addressing these challenges, along with the resource constraints of IoT devices, is essential for effective multi-cloud integration.  This paper proposes a hybrid decision-offloading model that integrates continuous and discrete decision-making. IoT devices must learn to make coordinated decisions regarding cloud server selection, task offloading ratios, and local computation capacity. This dual-layer decision-making process involves managing both continuous and discrete variables, along with inter-device coordination, which poses considerable challenges. To address these, we introduce a probabilistic approach that transforms discrete actions, such as selecting a cloud server, into a continuous domain. We further develop a Privacy-Aware Multi-Agent Deep Reinforcement Learning (PA-MADRL) framework that combines centralized training with distributed execution. This framework minimizes overall system costs by considering energy consumption and cloud server rental fees. Each IoT device operates as an agent, autonomously learning efficient policies while alleviating its computational burden.  Experimental results demonstrate that the PA-MADRL framework effectively adapts to dynamic network conditions, learning optimal offloading policies. It significantly outperforms four state-of-the-art deep reinforcement-learning models and two heuristic methods, achieving lower system costs and improved resource efficiency.

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R. Udaya Nirmala Mary mail -

R. Santhosh mail

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

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Fortifying Cloud-Based ERP Solutions: A Secure and Efficient Integration Approach

Cloud-based Enterprise Resource Planning (ERP) systems have become essential to organizational operations in today's digital environment, acting as the cornerstone for managing sensitive corporate data. ERP system integration with third-party apps, however, poses serious security risks because businesses cannot afford data breaches or illegal access that could jeopardize financial records, operational integrity, and reputation. Because ERP systems are appealing targets for cybercriminals looking to obtain sensitive company data, ensuring secure data exchange is an urgent concern. ERP integration security is still a problem, despite the numerous security frameworks and measures that have been put forth. Current methods frequently fall short of effectively addressing new threats. To guarantee the safe and smooth integration of cloud-based ERP solutions with external systems, this study presents an extensible security framework. The framework reduces the risk of data interception and unauthorized access by utilizing functional and technical security measures to produce a strong, adaptable security model. To prevent data leaks and unauthorized changes, the implementation is divided into two phases: (1) securing outbound data flow from the ERP portal to third-party systems, and (2) securing inbound data flow from third-party systems into the ERP portal, which protects against malicious intrusions and breaches of data integrity.

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Udita Malhotra mail -

Ritu mail

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

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Dermatology Chatbot: An AI-Driven Solution for Accessible Skin Care

The emergence of chatbots in the healthcare sector is increasingly pivotal, as they provide rapid and accessible assistance for the early detection of diseases and medical guidance. This study delineates a sophisticated two-tier healthcare chatbot system that synergistically integrates deep learning for image-based skin disease classification with machine learning for symptom-driven disease prediction. The system, developed in Python, employs a Hybrid U-Net & Improved MobileNet-V3 model to accurately identify dermatological conditions from images, while a Decision Tree Classifier is utilized to forecast diseases based on user-reported symptoms. Through meticulous evaluation of user inputs, the chatbot facilitates interactive consultations that encompass severity assessments, disease predictions, and preventive recommendations. Rigorous cross-validation of the symptom-based models, alongside testing on a bespoke dataset of skin disease images, substantiates the efficacy of the proposed methodology, demonstrating commendable predictive accuracy. The chatbot exemplifies significant potential by amalgamating conversational artificial intelligence with a hybrid approach of Hybrid U-Net & Improved MobileNet-V3 for image classification and Decision Tree Classifier for symptom analysis, thereby enhancing the landscape of telemedicine and patient care.

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Surya A. mail -

Chantilyan M. mail -

Chukka Ganesh mail -

Padmesh G. mail -

Patrick A. P. mail -

Raakesh G. mail -

S. Malathi mail

link https://doi.org/10.54216/JISIoT.170101

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A deep learning-driven multi-layer digital twin framework with miot for precision oncology in cancer diagnosis

This study introduces a novel deep learning-driven multi-layer digital twin framework, underpinned by the Model-Integration-Optimization-Testing (MIOT) methodology, to advance precision oncology in cancer diagnosis. The innovation lies in integrating multi-layered data, including molecular, clinical, and imaging modalities, into a patient-specific digital twin ecosystem. By combining deep learning with the MIOT framework, the proposed approach enables dynamic and predictive modelling tailored to individual patient profiles, facilitating simulations of tumor progression, diagnostic insights, and personalized treatment optimization. Pre-processing pipelines standardize the heterogeneous data, while convolutional and Recurrent Neural Networks        (RNN) extract high-level features from imaging and sequential data, respectively. The MIOT framework ensures a systematic design process: deep learning architectures like U-Net, DenseNet, and transformers are employed for tasks such as tumor segmentation, classification, and survival prediction. Data integration pipelines connect the digital twin seamlessly with clinical diagnostic tools to ensure interoperability. Multi-objective optimization algorithms, including evolutionary strategies and reinforcement learning, guide the digital twin in recommending personalized diagnostic and therapeutic pathways. State-of-the-art performance is demonstrated by rigorous validation on benchmark datasets, which yielded 96.3% diagnosis accuracy, 94.8% sensitivity, and 95.6% specificity across many tumor subtypes.

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Golden Nancy mail -

E. Bhuvaneswari mail -

Venkatesan R. mail

link https://doi.org/10.54216/JISIoT.170102

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

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.

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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