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ICTACT Journal on Soft Computing
ISSN 0976-6561 · ICT Academy of Tamil Nadu · 590 artikel di indeks
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CNN-U-NET HYBRID MODEL WITH ADAPTIVE ATTENTION MECHANISM FOR MEDICAL IMAGE SEGMENTATION
Medical image analysis has improved significantly using deep learning, but accurate segmentation remains challenging due to variability in disease patterns, anatomical structures, and image quality. This paper proposes a Hybrid CNN-U-Net Model with Adaptive Attention Mechanism (AAM) for automated…
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ATTENTION-GUIDED JOINT CNN–NEURO-FUZZY LEARNING FOR EXPLAINABLE MULTILINGUAL SIGNATURE VERIFICATION
This paper introduces an attention-guided joint CNN–neuro-fuzzy learning framework for offline handwritten signature verification, in which convolutional feature learning and fuzzy membership optimization are performed simultaneously rather than through a conventional sequential pipeline…
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A MODIFIED APPROACH OF ANALYSING FEATURE EXTRACTION FROM PATIENT SENTIMENTS USING WORD EMBEDDING AND VECTOR REPRESENTATION
Sentiment analysis has been increasingly popular in the present digital era which attempts to analyse the consumer reviews acquired from websites, blogs and social media platforms. In hospitals and other healthcare organizations, understanding patient feedback helps to exceed in providing top-notch…
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PERFORMANCE ANALYSIS OF DEEPFAKE VIDEO DETECTION USING DEEP LEARNING MODEL
Now a days, due to advances in computer editing tools, the creation and manipulation of fake content of images, videos, and audio has become quickly and easily accessible. Deepfakes are synthetic media created using Artificial Intelligence (AI), often with advanced deep learning models, that depict…
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HYBRID NEURO-FUZZY GENETIC FRAMEWORK FOR EARLY WARNING SYSTEMS USING SOIL MICROCLIMATE DATA ANALYTICS
The agricultural sector has experienced increasing uncertainty due to the climate variability and soil condition fluctuations. The need for an early warning system has become essential for improving the crop resilience and sustainability. Traditional data-driven approaches have shown limitations in…
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DEEP LEARNING AND MACHINE LEARNING BASED HYBRID OPTIMIZATION FRAMEWORK FOR ENHANCED WEBSITE PERFORMANCE TUNING
The performance of modern websites has become a critical factor in determining user engagement, service reliability, and computational efficiency. Background studies have shown that increasing traffic load, dynamic content delivery, and heterogeneous user behavior have significantly reduced…
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HYBRID DATA MINING FRAMEWORK FOR INTEGRATING CROP YIELD FORECASTING WITH WEATHER-BASED PUBLIC HEALTH RISK SURVEILLANCE
Agricultural productivity and public health are closely influenced by climatic variability, which has significantly affected both crop yield patterns and disease outbreaks. Extreme weather conditions have created the environmental factors that have increased the spread of vector-borne and…
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DATA MINING FRAMEWORK FOR ASSESSING RURAL RESPIRATORY DISEASE RISK FROM AGROCHEMICAL DRIFT USING WEATHER–CLINICAL DATA
Agricultural intensification has increasingly relied on the widespread application of agrochemicals, which have often dispersed beyond targeted farmlands through atmospheric drift. This environmental exposure has raised concerns regarding respiratory health among rural populations. Previous…
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AN ENHANCED CARSA FRAMEWORK FOR SECURE AND EFFICIENT ACCESS TO OUTSOURCED CLOUD DATA
Sensitive data exposure on the Internet has increased due to the quick development of Internet-based services and applications. Users can save money and save time by using cloud storage instead of keeping local storage, however there are significant privacy and security issues. Data owners may…
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AN EXTENSIVE ANALYSIS OF THE SOFTWARE COMPANIES EFFECTIVENESS IN INDIA
India's software sector contributes significantly to GDP and has a substantial share of the export market. This study ranks the performance of selected Indian software companies according to their technical efficiency. The company’s efficiency is evaluated using the input-oriented DEA technique…
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TRUST-AWARE FEDERATED LEARNING WITH SOFT COMPUTING FOR PRIVACY-PRESERVING HEALTHCARE ANALYTICS
The rapid adoption of the data-driven healthcare analytics has raised serious concerns regarding the patient privacy, data integrity, and collaborative intelligence across distributed medical institutions. Traditional centralized learning approaches have relied on extensive data sharing that has…
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BIO-INSPIRED METAHEURISTIC FRAMEWORK FOR HYPERPARAMETER OPTIMIZATION IN GRAPH NEURAL NETWORKS
Graph Neural Networks (GNNs) have emerged as an effective paradigm for learning from graph-structured data in domains such as social network analysis, bioinformatics, and recommendation systems. However, the performance of a GNN has remained highly sensitive to the selection of hyperparameters…
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QUANTUM-INSPIRED EVOLUTIONARY MODEL FOR ACCURATE AND ROBUST BRAIN TUMOR SEGMENTATION
Brain tumor segmentation has remained a critical task in medical image analysis, as accurate delineation has directly supported diagnosis, treatment planning, and clinical decision-making. Conventional deep learning approaches have achieved notable success; however, they have often struggled with…
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HYBRID ANT COLONY OPTIMIZATION WITH GRAPH NEURAL NETWORKS AND RELIEFF FOR ROBUST FEATURE SELECTION IN MEDICAL DATA ANALYSIS
In the field of medical data analysis, the major challenges are high dimensionality and complexity due to temporal behavior of medical datasets. Feature selection is crucial to overcoming these challenges since it enhances interpretability, reduces processing expenses, and boosts model performance…
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INVESTIGATING INTENSIFIER EFFECTS IN LOW-RESOURCE LANGUAGES USING CONVENTIONAL AND DEEP LEARNING MODELS
In this study, two machine learning models, Long Short Term Memory (LSTM) and BERT are used to predict intensifiers in Malayalam sentences. Both models were trained to detect intensifiers using part-of-speech (POS) tags, and BERT regularly outperformed more straightforward models like Naive Bayes…
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CNN-GRU MODEL FOR CREDIT CARD FRAUD DETECTION
Financial fraud in online credit card transactions poses significant challenges due to its increasing prevalence and the highly imbalanced nature of transactional data. This paper proposes a hybrid deep learning framework combining one-dimensional convolutional neural networks (CNN) and gated…
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MULTI-OBJECTIVE NEURO-FUZZY ENERGY MANAGEMENT CONTROLLER FOR SUSTAINABLE SMART CITY SYSTEMS
The rapid urbanization that has occurred across the globe has intensified the demand for energy-efficient infrastructures in the smart city domain. Conventional control strategies have struggled to address the dynamic, nonlinear, and uncertain nature of urban energy systems. Intelligent controllers…
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ATTENTION-GUIDED HYBRID LSTM–CNN FRAMEWORK FOR EARLY CARDIAC ANOMALY DETECTION
Wearable physiological sensors have enabled the continuous acquisition of cardiac signals that has supported early health monitoring outside clinical environments. However, the variability, noise, and temporal complexity of wearable signals have limited the reliability of conventional analytical…
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SWARM-OPTIMIZED DEEP NEURO-FUZZY FRAMEWORK FOR ROBUST MULTIMODAL MEDICAL IMAGE FUSION
Medical image fusion has played a critical role in clinical diagnosis by integrating complementary information from multi modal sources such as MRI, CT, and PET. Conventional fusion techniques have suffered from information loss, spectral distortion, and weak adaptability under complex anatomical…
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EFFECTIVE RNN BASED FEATURE INTERACTION MODEL USING CLOUD DATABASES
Identification of Feature interaction is the challenging task particularly for high dimensional database in the research field of feature selection which plays a major role in Machine Learning. “Effective RNN based Feature Interaction Model using Cloud Databases” is a Recurrent Neural Network based…