International Journal of Communication Systems and Network Technologies
International Journal of Communication Systems and Network Technologies (IJCSNT) (ISSN: 2053-6283 | DOI: 10.18486/ijcsnt) is an international, peer-reviewed, open-access scholarly journal dedicated to publishing high-quality original research articles, review papers, and short communications in the fields of communication systems, networking, computer science, information technology, and emerging engineering technologies. The journal aims to promote scientific innovation, facilitate the exchange of knowledge, and support academic and industrial research by providing a global platform for researchers, academicians, industry professionals, and practitioners. IJCSNT is committed to maintaining the highest standards of research integrity, publication ethics, and scholarly excellence while maximizing the visibility and accessibility of published research through its open-access publishing model. Peer Review Process All submitted manuscripts undergo an initial editorial screening to evaluate their originality, technical quality, relevance, and compliance with the journal's scope and ethical policies. Manuscripts that successfully pass this stage are subjected to a rigorous double-blind peer-review process conducted by independent experts in the respective field. Based on the reviewers' recommendations, the Editorial Board may accept, request revisions, or reject the manuscript. Authors are expected to disclose any sources of research funding and any potential conflicts of interest to ensure transparency throughout the publication process. IJCSNT follows internationally recognized publication ethics and best practices recommended by COPE (Committee on Publication Ethics), ICMJE (International Committee of Medical Journal Editors), DOAJ (Directory of Open Access Journals), and OASPA (Open Access Scholarly Publishers Association), ensuring a fair, transparent, and ethical publishing environment.
Machine Intelligence Research Labs, Gwalior
Neuromorphic Edge Intelligence in Collaborative Pipelines: An Energy-Latency Trade-off Analysis
Neuromorphic processors that execute spiking neural network (SNN) inference through event-driven, spike-based computation achieve milliwatt-scale power consumption and sub-millisecond latency, properties that make them attractive for always-on edge intelligence. However, no systematic framework exists for determining when neuromorphic inference at the device level outperforms offloading to conventional edge or cloud accelerators within a collaborative pipeline. This paper addresses that gap by proposing a four-tier collaborative architecture that explicitly distinguishes the neuromorphic edge from the general-purpose edge and by deriving an energy-latency-accuracy offloading decision boundary between these tiers. We compare five neuromorphic platforms (Loihi 2, SpiNNaker 2, Akida, TrueNorth, DYNAP-SE) against three conventional edge processors using published benchmark data, revealing a 10--100$\times$ energy advantage for spiking inference on sparse workloads alongside a 3--10 percentage point accuracy gap that constrains deployment. Three application case studies, autonomous vehicle perception, industrial IoT predictive maintenance, and wearable health monitoring, ground the framework in concrete deployment scenarios and show how the offloading boundary changes across domains. We further analyze the simulation-to-hardware gap in SNN training as a key factor governing the accuracy constraint. The proposed framework provides system designers with a quantitative tool for placing neuromorphic inference within heterogeneous edge--cloud deployments.
Copyright (c) 2026 Irum Nehvi, Ghazanfar Latif, DNFA Iskandar https://creativecommons.org/licenses/by-sa/4.0
Privacy-by-Design Data Engineering Framework for Multi-Cloud and Hybrid Data Platforms
Enterprises are heading into multi-cloud and hybrid cloud environments, which have enhanced capabilities for enterprise data management, but have also complicated privacy and security issues, governance models, and inter-cloud communication and regulatory compliance. The paper introduces an Enterprise Data Engineering Architecture for Privacy-by-Design that combines privacy-aware data ingestion, adaptive policy enforcement, secure data transformation, governance management, compliance verification and intelligent resource orchestration into a single framework. The framework is tested for effectiveness against the TPCx-BB (BigBench) benchmark dataset to give it a realistic test with enterprise workloads of structured, semi-structured and unstructured data. Experimental results show that the proposed framework achieves 99.11% of privacy compliance rate, 2.27 s of query execution time, 17.84 GB/s of data processing throughput, 63 ms of access latency, 331 MB of communication overhead, 99.18% of governance consistency, 99.14% of metadata synchronization accuracy, 99.08% of policy enforcement accuracy, 90.8% of resource utilization and 1.00 of scalability compliance index which exceed most of the evaluation metrics used in comparing with existing enterprise data engineering platforms. Moreover, framework shows only 0.46% privacy leakage, 99.24% unauthorized access detection, 99.73% secure data transfer, 6.18% encryption overhead, 99.58% success rate of the audit and 0.37% success rate at policy violations, thus reinforcing its capability of providing secure, privacy-preserving, and scalable enterprise data engineering for enterprise data and services in modern multi-cloud and hybrid cloud environments.
Copyright (c) 2024 Harshavardhan Peddireddy https://creativecommons.org/licenses/by-sa/4.0
Adaptive Spectrum Sensing in Cognitive Radio Networks Using Reinforcement Learning
Cognitive Radio Networks (CRNs) are potential solutions to the problem of spectrum scarcity through opportunistic access to unutilized frequency bands. But the traditional methods of spectrum sensing like energy detection, matched filtering, and cyclostationary detection usually exhibit constraints in low signal-noise ratio (SNR), noise uncertainty, and multipath fading conditions. In this paper, a proposal is made of an adaptive spectrum sensing framework, which utilizes the reinforcement learning (RL) and deep reinforcement learning (DRL) models to make an intelligent spectrum decision. This framework combines the spectral feature-extraction techniques of convolutional neural networks (CNNs), the channel-optimization techniques of Deep Q-Networks (DQN), and the multi-agent RL approach that utilizes cooperative sensing between distributed cognitive radios. Additionally, there is also a transfer learning mechanism to allow fast adaptation in the heterogeneous environment. Evaluations through simulations-based datasets and benchmark of the proposed approach have shown that it is capable of significantly enhancing the detection probability, the efficiency of spectrum utilization, and the resilience in the presence of uncertainty and outperforms classical baselines. The findings make the presented framework a scalable and effective solution to large-scale CRNs, which provides a better reliability and efficiency in the ever-changing wireless communication conditions.
Copyright (c) 2026 Sachin B, Safia Banu M, Dr S Asha https://creativecommons.org/licenses/by-sa/4.0
EfficientNetB0-Based Deep Learning Framework for Automated Eggplant Leaf Disease Classification
Early and accurate detection of plant diseases is crucial for enhancing crop yield and sustainable agricultural practices. This work presents an automated classification of eggplant leaf diseases using the EfficientNetB0 deep learning architecture. The model is built to detect four different classes, namely Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, and Mosaic Virus Disease. A publicly available dataset was used, and pre-processing all images in terms of resizing and normalization was done to match the input requirements of EfficientNetB0. Transfer learning using ImageNet pre-trained weights was used to improve feature extraction and decrease the training complexity. The dataset was split into train, validation, and test data using a stratified split strategy. Experimental results show that the accuracy of the overall classification is 88.5% with balanced precision, recall, and F1-scores in all classes. The confusion matrix analysis proves reliable class-wise performance with minimal misclassification. The results show that EfficientNetB0 is an effective and computationally efficient method for eggplant leaf disease detection, and has great potential to be used in real-world agriculture.
Copyright (c) 2026 Eshika Jain https://creativecommons.org/licenses/by-sa/4.0
RTL Design of a CNN-Based FPGA Accelerator for Handwritten Digit Recognition
Handwritten digit recognition plays a key role in areas like mail sorting, banking, and document processing. Software-based Convolutional Neural Networks (CNNs) achieve high recognition accuracy, but their computing power and energy needs limit their use in embedded systems. This paper presents the design and RTL-level simulation of a CNN-based hardware accelerator for handwritten digit recognition targeting a Xilinx Kintex-7 FPGA. The accelerator implements a modified LeNet-5 CNN architecture entirely in Verilog using fixed-point arithmetic. It consists of convolution layers followed by ReLU activation and max pooling, along with a fully connected output layer. The CNN model is trained offline using Python, and the trained and quantized weights and biases are converted to fixed-point representation and mapped directly into the RTL design. A streaming, line-buffer-based architecture is employed to efficiently generate convolution windows and enable continuous data flow through the network. The design is verified at the RTL level using cycle-accurate simulation with MNIST test images, ensuring functional correctness of all network layers. The proposed accelerator achieves a classification accuracy of 91% on 1,000 MNIST test images while utilizing 12,709 Slice LUTs and 238 DSP slices, with only 796 trainable parameters. These results demonstrate that the RTL-based CNN accelerator provides an effective trade-off between recognition accuracy and hardware resource utilization, making it suitable for embedded FPGA-based inference. Hardware implementation and on-board validation are planned as future work.
Copyright (c) 2026 Vivek Rathi, Vedant Kulkarni, Harshal Ingle, Mrs. P.S. Agnihotri https://creativecommons.org/licenses/by-sa/4.0
6G Vehicular Networks: Vision, Standards, and Key Enabling Technologies for the Internet of Vehicles (IoV)
As 5G networks become fully developed, the pressure for fully autonomous driving combined with the delivery of captivating in-vehicle experiences has revealed serious shortcomings in terms of latency, reliability, and massive connectivity. In this paper, we propose a holistic vision for the use of 6G in Vehicular Networks, thus going beyond mere communication to an "Internet of Verticals" (6G-IoV) which virtualizes and orchestrates entire industry entities. We look into the standardization landscape changes with a specific highlight on the evolution from NR-V2X Side link to 6G protocols as per the 3GPP-Releases 20 and 21. The paper delineates five major enabling technologies: Terahertz (THz) communication for exchanging ultra-high throughput raw sensor data; Integrated Sensing and Communication (ISAC) for environmental awareness headlined by "native" without dedicated sensors; Reconfigurable Intelligent Surfaces (RIS) to solve blockage via server programmable propagation in urban canyons; On Terrestrial Networks (NTN) for 3D coverage experience with LEO satellites and UAVs; and Native AI for network autonomous orchestration and federated intelligence. In the end, the paper discusses research issues in security in decentralized vehicular ledgers and the physical-layer challenges of high-mobility 6G channels.
Copyright (c) 2026 Gaydaa AlZohbi, B. Deepanraj https://creativecommons.org/licenses/by-sa/4.0
User Behavior Analytics for Insider Threat using Transformer Based Approach
In the past, attacks stemming from the deliberate or inadvertent actions of employees within the organization did not typically worry people. Due to the frequent reports of data breaches involving many organizations and their own employees, businesses are growing increasingly concerned about the need to keep an eye on user’s behavior on the network. In light of this, this study suggests a method for user behavior analytics. It makes use of the CERT (version 4.2) insider threat dataset, a five-log file synthetic dataset developed for insider threat research. Various raw events are provided by these log files. The behavior analysis of every user in this work is carried out based on log sources. The structural behavior provided by CERT, is in structural format, which is converted into the contextual data by fine-tuning the Llama3.1 model. These contextual data along with the synthesized contextual data are used to train the SecureBERT model for classification purpose. The model train accuracy was observed to start from 67% when trained and test accuracy was obtained to be 76% with 86% precision on 2500 data samples. The model was then trained on over 16 thousand data, observing the accuracy started with 92% in first epoch and reached to nearly 99.99%, which shows large language models can perform better and accurate in contextual user behavior analysis in insider threat.
Copyright (c) 2026 Aarati Kumari Mahato, Roshan Pokhrel, Om Prakash Mahato https://creativecommons.org/licenses/by-sa/4.0
A Taxonomy of Machine Learning Approaches for Obstructive Sleep Apnea Detection and Management in Pregnancy: Methods, Challenges, and Future Directions
Despite the well-recognized association between obstructive sleep apnea (OSA) and adverse maternal and fetal outcomes, OSA remains one of the most underdiagnosed conditions during pregnancy. Physiological changes occurring throughout pregnancy, including hormonal fluctuations, upper airway alterations, and increased blood volume, reduce the effectiveness of conventional screening questionnaires designed for the general population. This review analyzes 38 selected studies from more than 100 publications to examine the application of machine learning (ML) and artificial intelligence (AI) techniques for detecting OSA in pregnant women. The reviewed approaches range from traditional questionnaire-based classifiers, achieving accuracies between 0.79 and 0.85, to advanced deep learning models based on physiological signals that report accuracies exceeding 0.92 and sensitivities up to 0.977. Multimodal approaches integrating physiological signals, anthropometric measurements, and clinical information consistently outperform single-modality models. Furthermore, explainable AI techniques such as SHAP and LIME improve clinical interpretability, while wearable and contactless sensing technologies facilitate continuous monitoring beyond laboratory environments. The review also identifies critical challenges, including the absence of standardized datasets, inconsistent apnea–hypopnea definitions, and limited prospective validation in diverse obstetric populations. Future progress requires standardized benchmarks, lightweight and transparent AI architectures, and fairness-aware model development to enable reliable clinical deployment.
Copyright (c) 2026 Vandana Roy, Shalini Stalin, Nikhar Vishwakarma, Anuj Nayak https://creativecommons.org/licenses/by-sa/4.0
New Deep Learning Models for Analyzing Largescale Medical Imaging Data with Precision
Introduction of new deep learning technique has revolutionised the area of medical imaging, especially when it comes to handling of massive datasets that are a feature of healthcare systems. This paper investigates the application of state-of-the-art deep learning methods used for processing and interpretation of large-scale medical imaging data repositories. The main aim of this work is to tackle the difficulties arising due to high size, variety, and intricacy of these datasets by utilising deep neural network skills. The study also explores the infrastructure needs necessary to manage large-scale medical imaging datasets in a big data paradigm. It talks about how scalable computer frameworks and reliable data management systems work together to analyse, analyse, and extract useful information from massive volumes of imaging data. In the context of using deep learning to medical picture analysis, ethical issues, regulatory compliance, and data protection and security techniques are also discussed. In order to increase diagnostic accuracy and improve healthcare outcomes, this research intends to give a thorough understanding of the state of the art, difficulties encountered, and developments in applying cutting-edge deep learning techniques for analysing large-scale medical imaging data in a big data environment.
Copyright (c) 2025 Ciro Rodriguez https://creativecommons.org/licenses/by-sa/4.0
Cloud Computing Malware Detection and Classification Using ANN Based Machine Learning Model
This summary outlines the investigation into detecting and categorizing cloud computing malware using Artificial Neural Network (ANN)-based Machine Learning models. The surge in cloud services has elevated the risk of malware attacks in cloud environments. This research explores the application of ANN-based ML techniques to bolster cloud system security, emphasizing robust malware detection through feature extraction, dataset preparation, and model training. Various ANN architectures are considered and evaluated against real-world cloud malware datasets, revealing the efficacy of ANN models in detecting and classifying cloud-based malware with promising accuracy and efficiency. The deployment of a neural network classifier for binary malware that classifies Windows Portable Executable (PE) files according to imported library function calls is the specific topic of this article. The applied model demonstrates its capacity to generalize against an independent set with an astounding 97.8% average accuracy, 97.6% precision, and 96.6% recall. These findings demonstrate the practicality of the suggested malware categorization technique for bolstering cloud security against changing cyberthreats.
Copyright (c) 2025 Anurag Sinha, Harsh Raj, Michael Wiryaseputra, Mini kumari, Bhaskar Singh, Ahmed Alkhayyat https://creativecommons.org/licenses/by-sa/4.0
A High-Performance EfficientNetV2-M Model for Robust Corn Leaf Disease Classification
Corn leaf infections are highly yield-reducing and need to be diagnosed for effective field management. This study proposes a deep learning-based corn leaf disease detection framework based on EfficientNetV2-M, in a transfer learning setting. A structured preprocessing pipeline was used, which involved image resizing (480 × 480 pixels), contrast enhancement with CLAHE in LAB color space, and ImageNet-based normalization for matching inputs with pre-trained weights. The data set was successfully collected from the existing 4,226 field-acquired images of corn leaves categorized into Healthy and Infected classes and divided into training, validation, and testing data samples with stratified sampling. The model was trained for 22 epochs and had an overall test accuracy of 91%. Performance analysis revealed good class-wise metrics, with a precision/recall score of 0.92/0.87 for healthy leaves and 0.89/0.93 for infected leaves. The confusion matrix showed a low level of misclassification, which confirmed the effectiveness of generalization under realistic conditions. The results show that efficientNetV2-M offers an accurate and scalable approach for automated screening of corn leaf disease, enabling precision agriculture and early intervention strategies.
Copyright (c) 2025 Pratham Kaushik https://creativecommons.org/licenses/by-sa/4.0
Reactive Power Optimization Strategy for Power Grid with High Proportion of Renewable Energy Based on Reinforcement Learning Algorithm
As the high proportion of renewable energy grid connection leads to intensified grid voltage fluctuations, traditional reactive power optimization methods have obvious deficiencies in dynamic regulation capability and real-time performance, making it difficult to cope with the voltage over-limit and network loss problems caused by the randomness of wind and solar output. To this end, this paper proposes a reactive power optimization strategy based on the deep deterministic policy gradient (DDPG) algorithm, constructs a hierarchical reinforcement learning framework that includes 12-dimensional state spaces such as node voltage, renewable energy output, and load rate, and continuous/discrete action spaces such as SVG and capacitor banks, designs a composite reward function that integrates voltage deviation, network loss cost, and equipment action penalty, and implements minute-level online training through the interactive interface between the power grid simulation platform PSS/E and Python. Tests in the improved IEEE 33-node system show that this method reduces the voltage deviation rate to 0.41% and the average network loss to 123.2kW, verifying that the strategy can effectively coordinate the collaborative control of new energy stations and traditional reactive equipment, providing a new way to solve the problem of dynamic reactive power optimization in high-penetration power grids.
Copyright (c) 2025 Hao Chen, Nan Yang, Zeliang Ma https://creativecommons.org/licenses/by-sa/4.0
An AI Intelligent Workforce Analytics Framework for Employee Performance Prediction Using Ensemble Learning
AI has changed workforce analytics by allowing companies to make decisions based on data and predictions instead of just descriptive insights. In this research, we introduce an AI-driven system that use ensemble learning techniques, specifically XGBoost and Gradient Boosting, to forecast an individual's job performance. To accurately predict employee outcomes, workforce variables, including performance determinants, behavioral indicators, and productivity metrics, are meticulously analyzed. The proposed methodology seeks to enhance forecast accuracy while ensuring interpretability, hence aiding managers in decision-making. The experimental results indicate that both models had the capability to capture intricate nonlinear connections within labor data. XGBoost got 51.2% accuracy with balanced precision and recall metrics, which shows that it can make predictions that are consistent but not very good. The Gradient Boosting model did better than XGBoost on all evaluation criteria, with an F1-score of 62.16%, a recall of 63.89%, an accuracy of 55.2%, and a precision of 60.53%. The findings show that Gradient Boosting is the greatest way to predict how well workers will do since it makes them more resilient and better at generalizing. The results show that AI models that use ensembles function well for workforce analytics and can help HR strategy. The results of this study show how useful machine learning-powered performance prediction systems can be for improving the effectiveness of organizations and making decisions based on facts.
Copyright (c) 2025 Subhash Kodiyil Raman, Nithya Sambamoorthy, Sreenivasa Babu Nidamanuri, Shanmuga Pria, Farhana Sultana, Rajalingam Arumuganainar https://creativecommons.org/licenses/by-sa/4.0
A Comprehensive Review of Speech Signal Processing, Voice Activity Detection, and Automatic Speech Recognition: Techniques, Challenges, and Future Directions
Speech signal processing has become one of the most critical enablers of modern human–computer interaction, under-pinning technologies ranging from voice assistants and telephony to clinical diagnostics and security systems. This paper presents a comprehensive and systematic review of the field, spanning three tightly coupled domains: (i) fundamental speech signal analysis and preprocessing, (ii) voice activity detection (VAD), and (iii) automatic speech recognition (ASR). We begin by examining the physical and mathematical foundations of speech production and perception, followed by a detailed treatment of classical and contemporary feature extraction methods, including Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), Perceptual Linear Prediction (PLP), filter-bank features, and learned representations produced by deep neural networks. For VAD, we critically compare energy-based, statisti-cal, model-driven, and end-to-end deep learning approaches with respect to accuracy, latency, and robustness under diverse noise conditions. In the ASR domain, we trace the evolution from hid-den Markov model (HMM) systems through hybrid HMM–deep-neural-network (DNN) architectures to fully neural sequence-to-sequence models, including connectionist temporal classifica-tion (CTC) and attention-based encoder–decoder frameworks. Benchmark performance metrics across standardised datasets (TIMIT, LibriSpeech, CHiME, VoxCeleb) are consolidated and discussed. We further identify persistent open challenges—environmental noise, speaker variability, low-resource languages, and real-time edge deployment—and outline promising research directions, including self-supervised pre-training, multimodal fusion, federated learning, and neuromorphic processing. The survey is intended to serve as a unified reference for researchers and practitioners working at the intersection of signal processing, machine learning, and human–computer interaction.
Copyright (c) 2025 Sapna Rajput, Stuti Pandey, Tanushka Mishra https://creativecommons.org/licenses/by-sa/4.0
Design of an Ultra-Wideband Two-Element MIMO Antenna with Improved Performance
In this paper an efficient two element ultra wideband MIMO antenna is designed. First single UWB is designed. Then two element MIMO antenna is designed and its characteristics are observed. Then using double couplet defected ground structure the antenna performance is improved interms of isolation, ECC, DG. The designed 2 element MIMO antenna with DGS operates over (2.09-12.01)GHz. Due to DGS the mutual copuling has reduced by 13.61dB and return loss improved by 10.1dB at X-band frequency. ECC close to zero and diversity gain nearly 10 are achieved for the MIMO antenna with DGS. The simulations are carried using HFSS software and simulation results are compared with measured results.
Copyright (c) 2025 Krishna Rao Pedada, Chaitanya Kumar Marpu, S. Akhila, T. Reshma, K. Tejasree, S Mythili https://creativecommons.org/licenses/by-sa/4.0
Evaluation of Street Building Color Quality Based on Artificial Intelligence and Street View Images - Taking the Duobao Road Historical and Cultural Block in Guangzhou as an Example
In the context of rapid urban renewal, the color style of buildings in historical and cultural blocks is vulnerable to constructive destruction, and it is urgent to establish a set of objective, efficient and quantitative evaluation methods that meet the needs of protection planning. To this end, this study proposes a set of evaluation frameworks for street building color quality. Firstly, high-resolution street view images of the Duobao Road historical and cultural district in Guangzhou are collected as the basic data source. Then, DeepLabv3+ (Deep Labelling version 3+) is used to accurately identify and extract the building facade areas in the image. In addition, multi-dimensional color features of the building facades are extracted and calculated. Finally, a comprehensive evaluation system for the color quality of street buildings is constructed that integrates objective quantitative indicators. The experimental results show that the overall color coordination score of the block buildings is high, and most of the main colors of the buildings meet the requirements of Guangzhou's traditional color spectrum (the highest matching degree is 92% and the error range is small), and the AI evaluation results are highly consistent with the expert evaluation. This method provides efficient and reliable technical support for the scientific evaluation and effective control of the architectural colors of historical and cultural blocks.
Copyright (c) 2025 Shi Yang https://creativecommons.org/licenses/by-sa/4.0
Research on Key Technologies of Terminals for Electric Power Satellite IoT Private Network System
To address the monitoring requirements of transmission lines in areas without public network coverage in Xinjiang, an integrated electric power satellite IoT terminal is developed. Using a GaN power amplifier and a three-level sleep/wake-up mechanism, the operating power consumption is ≤30W, standby is <1W, achieving 94.6% energy saving. Forward link uses QC-LDPC+BPSK, return link uses Turbo+QPSK+12x spreading, achieving a demodulation threshold as low as -17dB, meeting the -12dB target. Simulation verifies an end-to-end delay of 90-115ms, meeting the real-time requirements of electric power services.
Copyright (c) 2025 Gang Liang, Yongbo Yao, Peng Xie, Xin Wang, Jie Liu, Wei Zhang, Gang Zhao https://creativecommons.org/licenses/by-sa/4.0
A Systematic Review on Student Performance Analysis Using Machine Learning
In educational system, the student performance is a core component which pays a significant role in the development of any school/college. The student performance handling is very crucial in learning process and it is one of the significant factors of learning. In the field of research educational data mining (EDM), by using the data knowledge education system can be improved. Advanced machine learning (ML) methods can predict student’s performance with key features based on academic, behavioral, and demographic data. Significant works have predicted the student’s performance based on the primary and secondary data sets derived from the student’s existing data. These works have accurately predicted student’s performance but did not provide the metrics as suggestions for improved performance. In this paper, we present the review of literature on the field of early student performance prediction using machine learning and also discuss machine learning techniques with their advantages and disadvantages.
Copyright (c) 2025 Priyanka Asthana, Manish Maheshwari https://creativecommons.org/licenses/by-sa/4.0
The Impact of Machine Learning: Harnessing the Power of Developing Water Quality Prediction System
A water's distinctive colour is the result of the sun's rays interacting with the water's concentration and its constituents. If the water's colour changes, it means its qualities have changed, and the water is no longer fit for human consumption. Water contamination and natural catastrophes like floods and tsunamis have become more pressing issues in recent years. The use of polluted water is responsible for 35% of all fatalities on Earth. There is no safe way to drink polluted water, but one way to mitigate the problem is to check the water's predicted quality before drinking it. Utilizing filtration, water plants base their processes on characteristics such as pH, turbidity, temperature, hardness, etc., and with the help of IoT, which incorporates both software and hardware; it is possible to anticipate water quality. Predicting future water levels using machine learning is the major focus of this study. By analyzing the water's colour and quality, the system can determine if the sample is fit for consumption or other purposes. The model for water quality prediction is trained using Tensorflow, Keras, the suggested Learning based Quality Prediction System (LQPS), and Random Forest Classifier (RFC). This system is efficient, uses an image processing tool, and is cost-effective. It may be used as an initial level water quality check and works immediately. By utilizing mobile-captured and Google Earth photographs of water samples, it is possible to verify the proposed method of water quality prediction.
Copyright (c) 2025 R Ramya, S. Pradeep, B. Geetha, K. Selvi, V. Samuthira Pandi, D Shobana https://creativecommons.org/licenses/by-sa/4.0
Investigation the Impact of Data Pre-Processing and Dimensionality Reduction to Improve the Accuracy of Machine Learning
Data pre-processing stands as a fundamental stage in machine learning and data mining. Within this domain, normalization, discretization, and dimensionality reduction are widely recognized techniques. This research paper aims to investigate the impact of Min-max, Z-score, Decimal Scaling, and Logarithm to the base 2 on the accuracy of the J48 classifier, utilizing the NSL-KDD dataset. Experiments were conducted employing these methods, and their respective outcomes were systematically compared. Additionally, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were assessed for dimensionality reduction. Notably, a hybrid combination of PCA and LDA was explored, demonstrating an enhanced classification accuracy compared to the individual methods.
Copyright (c) 2025 Viranchee V. Dave, Shankarshan Panda https://creativecommons.org/licenses/by-sa/4.0
Unlocking the Potential Opportunities of Novel Routing Protocols over Wireless Sensor Network: A Comprehensive Evaluation
Sensor nodes in a network often have low power needs and communicate wirelessly. The challenge of creating a wireless sensor network routing system that uses minimal energy is a significant one. In wireless sensor networks, saving power is crucial to extending the life of the system. Large-scale wireless sensor networks (WSNs) include hundreds or thousands of sensor nodes into a single system. They weigh next to nothing, have a short battery life, and have a low primary storage capacity and computing capability. They can wirelessly monitor environmental or physical parameters. This research presents a performance analysis of many existing routing protocols and a suggested layered distinct tree framework for storing route information in a target tracking application. Multiple sensor nodes are deployed utilizing a comprehensive binary tree data structure to safely store the collected data. Wireless sensor networks can only use so much electricity because of their finite supply of batteries. This resulted in a drastic decrease in the network's efficiency. Therefore, this study presents a Wireless AdHoc Routing Protocol (WAHRP) for lowering network energy usage. The suggested technique is compared with the current routing protocol called AdHoc On-Demand Distance Vector (AODV) to evaluate the efficiency of the proposed scheme and the resulting part shows the right proof to it in plain manner. This technology is able to identify the precise location of a target, conserve energy, and lengthen the life of a network. The goal is to reduce the amount of power used by the network by putting idle terminals into sleep mode at predetermined times. Several uses place a premium on throughput and care little about latency. Which parameter is preferred ultimately depends on what the application wants. Having a thorough understanding of network architecture and routing protocols that is tailored to the needs at hand is crucial.
Copyright (c) 2025 T. Sripriya, R. Lathamanju, V. Arulkumar, Sudha K, Aswini E, Ragini Y P https://creativecommons.org/licenses/by-sa/4.0
Model Based on Machine Learning for Sensing Degree of Boredom
In a world where things change constantly, our educational frameworks have mostly remained the same. The usage of smartboards and innovative homeroom arrangements have been the only advancements in teaching methods. The most widely utilized method of instruction in Indian education to date is the "chalk and talk" method. Though displaying methods have helped progress technology, it is quite difficult to gauge or pinpoint an understudy's emotional state in a discourse. A speech's main goal is to educate the audience and guarantee that the material being taught is retained to the best standard. However, it is quite uncommon for an understudy who becomes tired during or midway through a talk to be able to remember more than half of the information spoken during that session. In this work, we want to explore possible setups that might help monitor and detect understudy emotions during a study hall lecture, such as Machine Learning. Drawing from many beliefs and concepts mentioned in the book, they provide a summary of the present state of workmanship study on this intriguing issue.
Copyright (c) 2025 Sonika Thapak, Manmohan Singh, Nikita Swarnkar https://creativecommons.org/licenses/by-sa/4.0
Construction of Highway Bridge Health Monitoring and Vehicle-Mounted Intelligent Operation and Maintenance System Based on Edge Computing Joint Clustering Algorithm
To address the bottlenecks of hidden and sudden structural damage to highway bridges under long-term heavy traffic, the inability of traditional central cloud monitoring to process massive amounts of vehicle-mounted mobile data in real time, and the difficulty in multi-source heterogeneous data fusion, this paper constructs a health monitoring and vehicle-mounted intelligent operation and maintenance system driven by edge computing and joint clustering. First, a three-layer collaborative architecture of "vehicle-roadside-edge cloud" is designed. Then, a distributed multi-view joint clustering algorithm is proposed, simultaneously optimizing bidirectional clustering of sample and sensor features, and exchanging centroids only through federated averaging between edge nodes. Finally, a vehicle-mounted terminal is developed to map damage level and location in real time to graded early warning and maintenance suggestions. Experiments show that the anomaly detection accuracy is 94.7%, alarm latency is 87ms, transmission volume is 0.41MB, a reduction rate of over 99.6%, and strong noise robustness. The integration of edge computing and joint clustering solves the problems of real-time performance and data silos, achieving low-latency and high-precision vehicle-mounted structural health operation and maintenance.
Copyright (c) 2025 Donghong Xue, Guijuan Tian, Yingchuan Lv, Yamei Zhou https://creativecommons.org/licenses/by-sa/4.0
Motion Artifact Removal Using Empirical Mode Decomposition Using Ks test Blind Source Separation and Wavelet Transform
For medical science image processing, the elimination of artifacts from physiological signals is an essential step. The acuteness in performance of healthcare technology has upgraded from the current hospital centric environment towards a portable ubiquitous approaches. The enormous cost of fine equipment and ensemble technologies has created a window for mathematical models that approximates the signal dynamics towards a superlative performance. The uncertainty in subsequent performance of these approaches introduced a dedicated research and past few decades have witnessed considerable improvement. In this paper an enhanced empirical approach to model the artifacts of a free signal is described followed by filtering mechanism using ICA and DDWT. The input EEG is a single channel and is converted into multichannel for ICA operations. The multi-channel EEG constructed using EEMD is filtered with fast ICA algorithm and DDWT is employed to reject any traces of artifacts left in signal. This system is tested on various platforms of evaluation and results pronounce the eligibility of proposed algorithm to stand on top of currently deployed algorithms on account of significant improvement in results.
Copyright (c) 2025 Vandana Roy, Shailja Shukla https://creativecommons.org/licenses/by-sa/4.0
Comprehensive Investigation of Routing Mechanism and Energy Consumption in Wireless Sensor Networks
Wireless ad hoc network and Sensor networks have gained popularity due to adaptability in all walks of communication life, especially when every device is going wireless. In this paper an exhaustive survey has been done to investigate the energy consumption and also to save the energy while transferring data from motes to cluster head and then to PAN coordinator. In this work routing algorithms have also been analysed critically for efficiency and power saving. This paper analyses the work in the area of routing, congestion and lifetime of the nodes in the sensor network and suggested methods to improve the parameters. Protocols like LEACH have been given due consideration while analysing the work.
Beyond Lithium Ions: Research on Next-Generation Battery Technology
Technological developments in batteries are essential to meeting the rising energy needs of contemporary society. This study investigates the drawbacks of the present generation of lithium-ion batteries as well as the possibilities for their replacement. It covers the safety, energy density, and useful applications of solid-state batteries in particular, highlighting their potential. The obstacles that must be removed in order for these new technologies to become widely used are also covered in the article. In the end, it highlights how crucial it is to carry out more research on next-generation battery technologies in order to create a sustainable and energy-efficient future.
Copyright (c) 2024 Atul Kumar https://creativecommons.org/licenses/by-sa/4.0
Smart City: Artificial Intelligence and Machine Learning Applications
When development, urban smart handle are supposed effectively cities to the brought problems by about rapidly increasing urbanization, maximize use, energy promote environmental sustainability, living and raise economic standards, and contemporary enable to people use technology communication for and information ease. with ICT the is smart-city essential paradigm to and plays a in key the role creation of policies, the process making of decisions, their and execution, provision the of useful services. explores This how article deep reinforcement learning (DRL), machine learning (ML), and artificial intelligence (AI) have shaped development the of smart cities. cutting-edge These methods are crucial for developing best the policies to address intricate the problems that with arise city smart projects. study The with concludes outline an future of possibilities and research problems, emphasizing the tremendous potential these of strategies achieving in the overall goal a of smart city.
Copyright (c) 2024 Geetha L, Ashwini Satyanarayana https://creativecommons.org/licenses/by-sa/4.0
Energy Optimization: Smart Grid Integration with Renewable Energy
Energy optimization important problems in a smart grid setting, with user experience improvement, controlling battery energy storage, and load scheduling, are tackled in this paper. The efficiency of controllers in maximizing energy generation, user comfort, load scheduling, and real-time battery backup scheduling depends on system inputs, including dynamic pricing signals, load arrival dynamics, battery charging and discharging dynamics, and processes for producing renewable and conventional grid energy. To explore these issues, the virtual queue safety (LOT) based Lyapunov optimization approach is used in a grid-connected green smart home with an HVAC load. The main goal is to reduce the total time-averaged energy cost and the cost of thermal discomfort over a longer period. Variations in the number of occupants in the house, desired temperature circumstances, electrical consumption, output from renewable energy sources, outside temperature, and power prices are all considered in this optimization. The suggested method creates and maintains four queues for the electrical energy storage system (ESS), EV charging, and interior temperature. The algorithm’s efficacy is verified through extensive simulations. Simulation results show that the suggested strategy satisfies the user’s energy and comfort needs while achieving real-time energy optimization and a 20.15% decrease in the time-averaged energy cost.
Copyright (c) 2024 Dr. N. M. G. Kumar, Dr. M. Kalyan Chakravarthi https://creativecommons.org/licenses/by-sa/4.0
Compact UWB Textile Antenna with Multi-Phase Evolution from Single to Dual Designs
In this paper, the design and performance analysis of ultra-wideband (UWB) antennas employing flexible textile substrates are presented for wearable wireless communication systems. A threephase design strategy is adopted, beginning with a single antenna element, followed by a dualelement configuration, and finally a stub-coupled dual arrangement to mitigate mutual coupling and improve impedance bandwidth. Textile materials including nylon, leather, cotton, and silk are utilized as dielectric substrates, and their impact on impedance characteristics, resonance behavior, and bandwidth is systematically investigated. The reflection coefficient S11 results demonstrate that substrate permittivity and dielectric losses play a crucial role in shaping antenna performance. Silk and nylon substrates exhibit deeper resonances with good impedance matching, while leather provides broader operating bandwidth. Cotton, due to its higher dielectric loss, yields comparatively shallow resonances and reduced efficiency. For dual-element designs, mutual coupling effects are evident, but the incorporation of a central stub significantly enhances port isolation, maintaining S11< −10dB across the 3.1–10.6 GHz UWB band. The antennas also maintain stable radiation patterns, acceptable gain, and conformal robustness under bending conditions. The proposed textilebased UWB antenna designs confirm the feasibility of low-cost, flexible, and reliable solutions for wearable and body-centric communication applications, with the stub-coupled configuration demonstrating superior performance among the investigated cases.
Copyright (c) 2024 D Ramesh Varma, K Padma Vasavi, M Padmini Chowdary, M Venkata Subbarao, G Challa Ram, D Girish Kumar https://creativecommons.org/licenses/by-sa/4.0
Beyond Speech Recognition: Unveiling the Capabilities of Voice Assistants
This article explores the evolutionary trajectory of voice assistants, surpassing traditional speech recognition to become versatile digital companions. With the integration of natural language understanding, multi-modal interfaces, and contextual awareness, these entities play pivotal roles in task automation, smart home integration, language translation, and accessibility, profoundly impacting user experience and daily life. As voice assistants continue to collaborate with third-party services, the article envisions an exciting future where these technologies redefine our interactions with digital assistants, shaping a new paradigm in technology evolution.
Copyright (c) 2024 Kumar. S, Ranjit Choudhary, Arpan Kumar Gupta, Prince Kumar, Amar Deep Kumar https://creativecommons.org/licenses/by-sa/4.0