Journal of Computing and Data Technology
Journal of Computing and Data Technology Journal of Computing and Data Technology is an international, peer-reviewed journal dedicated to publishing high-quality research and advancements in computing, computer science engineering, data science, machine learning, and emerging technologies. The journal aims to provide a platform for researchers, practitioners, and academicians to disseminate innovative ideas, theoretical developments, and practical applications in the field. Format of Publication: Online Language of Publication: English Subject: Engineering Scope Journal of Computing and Data Technology invites original research articles, review papers, and case studies in, but not limited to, the following areas: Artificial Intelligence and Machine Learning Big Data Analytics and Data Science Data Processing Techniques Cloud Computing and Edge Computing Internet of Things (IoT) Cybersecurity and Cryptography High-Performance Computing Quantum Computing Computational Intelligence Data Mining and Knowledge Discovery Blockchain Technology and Applications Software Engineering and Algorithms Computer Vision and Image Processing Human-Computer Interaction Robotics and Automation Natural Language Processing (NLP)
Krrish Scientific Publications Private Limited
A Systematic Review on Hybrid Deep Learning and Metaheuristic Optimization Techniques for Medical Image Segmentation and Classification
Medical image segmentation and classification have become fundamental components of modern intelligent healthcare systems due to their ability to support early disease diagnosis, treatment planning, prognosis evaluation, and computer-aided clinical decision-making. Recent advances in deep learning have significantly improved the capability of automated medical image analysis systems across multiple imaging modalities including X-ray, computed tomography, magnetic resonance imaging, ultrasound, histopathology, and retinal imaging. However, despite remarkable performance improvements, conventional deep learning models still face several limitations related to hyperparameter tuning, computational complexity, convergence instability, local minima stagnation, class imbalance, limited interpretability, and poor generalization under heterogeneous clinical conditions. To address these challenges, hybrid frameworks integrating deep learning with metaheuristic optimization techniques have emerged as a promising research direction for improving segmentation accuracy, classification robustness, feature optimization, and computational efficiency. This review article covers the historical evolution of intelligent medical image analysis, theoretical foundations of optimization-driven learning, taxonomy of deep learning architectures, and the role of evolutionary and swarm-based optimization algorithms including genetic algorithms, particle swarm optimization, grey wolf optimizer, firefly algorithm, whale optimization, ant colony optimization, Bayesian optimization, and reptile search optimization. Comparative analysis of datasets, evaluation metrics, computational complexity, convergence behavior, and clinical deployment challenges is also presented. Finally, open research challenges and future directions are identified toward trustworthy, interpretable, scalable, and autonomous AI-driven medical imaging systems for next-generation intelligent healthcare applications.
Explainable Artificial Intelligence (XAI).
Copyright (c) 2026 Ramadevi Potharla, Bala Krishnama Manohar, N. Bhargavi, Valluru Prathyusha, P. Shailaja (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Comprehensive Systematic Review of AI-based Network Intrusion Detection Systems: Techniques, Datasets, Challenges, and Future Research Directions
The rapid expansion of cloud computing platforms, Internet of Things (IoT) ecosystems, edge devices, software-defined networks, and distributed enterprise infrastructures has significantly increased the complexity and scale of modern cybersecurity environments. Traditional signature-based intrusion detection systems are increasingly ineffective against sophisticated cyber threats such as zero-day attacks, advanced persistent threats, ransomware propagation, botnet activities, and encrypted malicious traffic because they rely heavily on predefined attack signatures and static rule-based detection mechanisms. Consequently, AI driven intrusion detection systems have emerged as promising solutions for intelligent threat detection, adaptive cybersecurity analytics, and real-time network defense. This paper presents a comprehensive systematic literature review of AI based network intrusion detection systems with particular emphasis on machine learning, deep learning, federated learning, and intelligent anomaly detection frameworks. Furthermore, the study evaluates benchmark cybersecurity datasets including NSL-KDD, CICIDS2017, UNSW-NB15, DARPA, and TON-IoT datasets to investigate their effectiveness, scalability, realism, and applicability for modern intrusion detection research. Comparative analysis indicates that deep learning and hybrid AI techniques significantly improve intrusion detection accuracy, adaptive threat detection, and real-time cybersecurity analytics compared with traditional approaches. The paper further discusses major challenges including adversarial attacks, encrypted traffic analysis, dataset imbalance, explainability, computational overhead, and concept drift. Emerging research directions such as federated intrusion detection, Transformer-based cybersecurity, graph neural networks, self-supervised learning, and explainable AI are also critically analyzed.
Copyright (c) 2026 Mehdi Gheisari, Zhou Pingmei, Basheer Riskhan, Malusi Sibiya, Muhammad Faizan Khan, Seyed Kazem Gheblezadeh (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Artificial Intelligence-Based Data Integration Framework for Real-Time Cross-Source Data Harmonization
The rapid growth of digital data ecosystems has intensified the need for efficient methods that can integrate and harmonize heterogeneous data sources in real time. Conventional rule-based data integration pipelines often struggle to handle schema heterogeneity, semantic inconsistencies, and incomplete records across distributed data repositories. This study proposes an artificial intelligence based data integration framework designed for real-time cross-source data harmonization across heterogeneous data environments. The proposed framework integrates schema matching, entity resolution, and data transformation modules into a unified pipeline that combines lexical similarity, semantic normalization, and attribute-profile consistency analysis. To evaluate the effectiveness of the framework, experiments were conducted using a dataset extracted from the World Development Indicators (WDI) repository. Since the available dataset represented a single source extract, a heterogeneous secondary schema was constructed through controlled attribute renaming and semantic perturbation in order to emulate realistic cross-source integration scenarios. The experimental evaluation assessed schema matching accuracy, entity resolution performance, integration latency, and data completeness. The results demonstrate that the proposed AI framework significantly outperforms conventional integration baselines. Specifically, the framework achieved a schema matching accuracy of 1.000 compared to 0.857 for similarity-based matching and 0.571 for lexical rule-based matching. In the entity resolution task, the framework obtained perfect precision, recall, and F1-score, while baseline approaches exhibited substantial performance degradation under heterogeneous naming conditions. Although the proposed system incurred a modest increase in computational latency (12.4 ms) relative to lightweight baselines, the latency remained within real-time operational limits. Additionally, the harmonization process improved dataset completeness from 91.67% to 100%.
Copyright (c) 2026 Sarvendra Aeturu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Integrated Framework for Phishing Threat Mitigation via Simulation-Driven Analysis, Email Header Forensics, and URL Intelligence
Phishing attacks continue to represent a dominant vector for cyber intrusions, exploiting human vulnerabilities and systemic weaknesses in email communication infrastructures. This study presents an integrated and simulation-driven cybersecurity framework that combines phishing campaign emulation, email header forensics, and URL intelligence analysis to enable proactive threat detection and mitigation. The proposed approach leverages open-source platforms to systematically replicate real-world phishing scenarios, thereby facilitating controlled experimentation and behavioral analysis. Email header inspection is employed to extract and validate metadata attributes such as sender authenticity, routing paths, and authentication protocols, such as Sender Policy Framework (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) are used to verify the authenticity of email senders and detect spoofing attempts. Concurrently, URL scrutiny mechanisms incorporate blacklist verification, domain reputation assessment, and structural pattern analysis to detect malicious or obfuscated links. The framework is designed to enhance situational awareness by correlating insights derived from simulation outputs, header anomalies, and URL threat indicators. Experimental evaluation demonstrates improved detection accuracy and response efficiency when compared to isolated analysis techniques. Furthermore, the integration of feedback-driven awareness mechanisms strengthens organizational resilience against evolving phishing tactics. The proposed methodology not only provides a comprehensive analytical perspective on phishing attack vectors but also contributes a scalable and cost-effective solution for real-world deployment in enterprise security ecosystems.
Copyright (c) 2026 Mehdi Gheisari, Sabitha Banu, Saman Khammar, Mustafa Ghaderzadeh, Saeed Lotfi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Data Processing and Advanced Analytics for Scalable Knowledge Discovery in Complex Data Environments
The rapid proliferation of heterogeneous and high-dimensional data across domains has amplified the demand for efficient data processing and robust analytical frameworks. This paper presents a comprehensive study on integrating data processing and analysis as a unified paradigm to enable effective knowledge discovery and intelligent applications. Data processing encompasses systematic techniques for acquisition, cleaning, transformation, and integration of raw data into consistent and reliable forms. Coupled with advanced analytical approaches, including statistical modeling, machine learning, and deep learning, these processes collectively transform unstructured information into actionable insights. The proposed perspective emphasizes scalable pipelines, real-time processing frameworks, and outlier-robust mechanisms to ensure reliability across large and dynamic datasets. Furthermore, the integration of descriptive, predictive, and prescriptive analytics demonstrates the potential for enhanced decision-making in critical sectors such as healthcare, energy systems, finance, and governance. The paper also highlights emerging challenges, including interpretability, privacy preservation, and ethical considerations, while underscoring future research opportunities in quantum data analysis and federated learning. By bridging data processing and analysis, this study advocates a holistic approach that fosters transparent, adaptive, and scalable knowledge discovery, ultimately strengthening the role of data-driven intelligence in addressing complex real-world problems.
Copyright (c) 2025 Benneth Oyinna, Peter David Udo, Irfan Nurhidayat, Abdul Raqib Muslimyar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Deep Learning Approach to Early Drowning Detection for Child Safety using ResNet and Flower Pollination Algorithm
Drowning is one of the top five worldwide causes of mortality for kids between the ages of four to fourteen. According to World Health Organization statistics, drowning is the third most common cause of unintentional fatalities. The need to create a drowning detection system to save swimmers especially children is growing. There are several drowning detection systems incorporating distinct technological systems such as wearable sensors, vision based monitoring and AI driven surveillance. Wearable sensors such as heart rate monitoring and accelerometers offer continuous monitoring but these existing systems have their own limitations like intrusiveness and poor visibility. To address these challenges this study offers a deep learning and computer vision-based early drowning detection technique. An overall accuracy of 99.4% has been achieved on our dataset by using pre-trained models including CNN-ResNet50, ResNet18, and Flower Pollination Algorithm. The study contributes to the advancement of intelligent drowning surveillance systems thus abridging the gap in the attainment of more efficient and reliable drowning detection systems.
Copyright (c) 2025 Muhammad Aftab Hayat, Yang Guotian, Shams ur Rehman (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Emotional Expressiveness in Voice Conversion Using Seq2Seq and CycleGAN
Emotional voice conversion (EVC) aims to transform the emotional characteristics of speech while preserving speaker identity and linguistic content, and plays a critical role in affective computing, expressive speech synthesis, and human--computer interaction. Despite recent progress, existing EVC approaches often struggle to jointly model long-term temporal dependencies and achieve perceptually realistic spectral transformations, particularly in non-parallel training scenarios. To address these challenges, this paper proposes a high-fidelity emotional voice conversion framework that integrates Sequence-to-Sequence (Seq2Seq) temporal modeling with Cycle-Consistent Generative Adversarial Networks (CycleGAN). The proposed architecture operates entirely in the Mel-spectrogram domain. A Seq2Seq encoder--decoder with attention is first employed to capture long-range temporal dependencies and generate a coarse emotion-aware spectral representation. Subsequently, a CycleGAN-based refinement module enhances spectral realism and emotional expressiveness through adversarial and cycle-consistent learning, without requiring parallel emotional speech data. A neural vocoder is finally used to reconstruct the time-domain waveform from the refined Mel-spectrogram. The proposed framework is evaluated on the Emotional Speech Dataset (ESD) using objective metrics including Mel-Cepstral Distortion (MCD), fundamental frequency (Fo) root-mean-square error (RMSE), and structural similarity index (SSIM), along with subjective listening evaluations. Experimental results demonstrate that the proposed Seq2Seq--CycleGAN model outperforms conventional Seq2Seq-only and CycleGAN-only baselines in terms of emotional expressiveness, speech naturalness, and speaker similarity, confirming the effectiveness of jointly leveraging temporal modeling and adversarial spectral refinement for high-quality emotional voice conversion.
Copyright (c) 2025 Ebrahim E. Elsayed, Abdullayev Vugar, Hayal Mohammed R., Didi Faouzi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Graph Neural Network-Based Framework for Real-Time Financial Fraud Detection in Digital Payment Ecosystems
The rapid growth of digital payment ecosystems, which include mobile wallets, UPI platforms, online banking channels, and merchant-integrated gateways, has led to a big rise in both the number of transactions and the complexity of financial fraud. Standard systems for finding fraud mostly use rule-based methods or independent transaction-level classifiers. These systems can't pick up on the complicated relational and temporal dependencies that are common in modern fraud patterns. This research presents a Graph Neural Network (GNN)-based real-time fraud detection framework that conceptualizes the digital payment ecosystem as a dynamic, multi-relational graph consisting of users, devices, merchants, IP addresses, and transactional interactions. The framework combines Relational Graph Convolutional Networks, Graph Attention Networks, and Temporal Graph Networks to learn behavioral patterns that change over time, in context, and in structure. A hybrid supervised–unsupervised scoring module is used to figure out how likely fraud is. This module can find both known and new types of attacks. A lot of tests on big synthetic payment datasets show that the proposed model does much better than traditional machine learning and deep learning baselines in terms of AUC-ROC, precision, and F1-score. It also has an inference latency of less than 50 ms, which makes it good for real-time use. The results show that GNN-based methods are effective for next-generation financial systems that need to be safe, scalable, and smart when it comes to stopping fraud.
Copyright (c) 2025 Ramakrishna Penaganti (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bridging Mind and Machine: Enhancing Human Potential Through Self-Regulation and Brain-Computer Interfaces
In an era defined by accelerating neurotechnological innovation and heightened cognitive demands, the pursuit of human enhancement is reaching unprecedented heights. This paper explores two converging frontiers of optimization: internal strategies of mind management and external augmentation via brain-computer interfaces (BCIs). Drawing from recent empirical research, mathematical models, and startup ecosystems, also analyzed how self-regulation techniques such as mindfulness, reframing, and attentional training intersect with emerging neurotechnology to expand cognitive capacity, productivity, and mental well-being. Clinical applications of BCIs, including Deep Brain Stimulation (DBS) and Responsive Neurostimulation (RNS), are evaluated alongside non-invasive wearables, with comparative insights into efficacy and patient outcomes. Furthermore, the study examines advanced BCI architectures, ethical dilemmas in military and defense applications, and emerging strategies within the commercial neurotechnology market. In parallel, mathematical optimization frameworks and neuro-algorithmic models are presented to bridge the disciplines of neuroscience and artificial intelligence, highlighting their synergistic potential for cognitive enhancement. By synthesizing interdisciplinary insights, the paper outlines future research directions and offers a critical roadmap for responsible innovation in the medical and business sectors. Ultimately, this study positions the integration of self-directed mental practices and neurotechnological tools as a dual engine for unlocking human potential.
Copyright (c) 2025 Mohammad Parhamfar, Milad Taheri, Zahir Bandegani, Ghita Lazrek, Hajar Fares (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Study on Artificial Intelligence and Cloud Computing Assistance for Enhancement of Startup Businesses
Startups are dynamic enterprises that often operate under high uncertainty and resource constraints while seeking rapid innovation and scalability. Technological advances, particularly in Artificial Intelligence (AI) and Cloud computing, have opened transformative opportunities to overcome these challenges. AI tools, such as machine learning algorithms, chatbots, recommendation engines, and predictive analytics, enable startups to optimize decision-making, automate workflows, and enhance customer engagement. Meanwhile, cloud storage systems and platforms offer cost-efficient, scalable infrastructure that mitigates the need for heavy upfront investments and allows businesses to adapt quickly to market demands. This paper explores how AI and cloud computing synergistically drive the growth of startup businesses by analyzing their applications, benefits, and challenges. A detailed literature survey elucidates existing research and practical implementations. Furthermore, this work proposes a methodology demonstrating the integration of AI tools with cloud storage systems to enhance key business processes. A case study is discussed to illustrate tangible outcomes, and a comparative analysis highlights the effectiveness of AI and cloud computing for the startup business. The findings underscore that startups leveraging these technologies achieve superior operational efficiency, market agility, and competitive advantage. Future research avenues include sector-specific strategies, ethical considerations, and long-term performance analysis.
Artificial Intelligence, Cloud computing, Natural Language Processing, Cloud storage systems, Business Enhancement, Scalability
Copyright (c) 2025 Kiran Saripudi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Image Fusion through Multi-Scale Adaptive Weighting and Post-Fusion Optimization
Image fusion plays a vital role in modern image processing by integrating complementary information from multiple source images into a single, enriched representation. This capability is critical in fields such as medical imaging, remote sensing, and surveillance. However, traditional fusion methods—such as pixel averaging and wavelet-based techniques—often struggle to preserve fine details or adapt to varied image content, leading to artifacts and degraded quality. Deep learning-based approaches offer improvements but require extensive datasets and high computational resources, limiting their use in real-time or resource-constrained environments. To address these limitations, this paper proposes a novel image fusion framework combining multi-scale adaptive weighting with post-fusion enhancement. The method utilizes multi- resolution decomposition to extract frequency components, assigning perceptual-based adaptive weights based on local salience and structural relevance. A dedicated enhancement stage further improves contrast, sharpness, and detail retention. Experimental results across diverse datasets show that the pro- posed method outperforms conventional techniques, achieving higher mutual information (2.85), structural similarity (0.92), and PSNR (34.6 dB), while maintaining superior visual quality. This framework provides an efficient and robust solution suitable for real-world deployment, advancing the state-of-the-art in image fusion.
Copyright (c) 2025 Sreeja Akuthota (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture Features
Dravidian languages, including Tamil, Telugu, Kannada, and Malayalam, have complex orthographic structures, making script identification challenging particularly for camera-based document images. This study proposes a hybrid approach that combines deep learning and texture-based methods for robust script recognition. The GoogLeNet convolutional neural network (CNN) model is used to extract deep features, while local binary patterns (LBP) and histogram of oriented gradients (HOG) capture texture characteristics. These features are fused and classified using support vector machine (SVM) classifier. Results show that CNN features alone achieve 84.50% accuracy, LBP achieves 85.90%, and HOG achieves 76.10%, while their fusion significantly improves accuracy to 92.10%. The combination of CNN and HOG features reaches 95.00% accuracy, demonstrating the effectiveness of integrating deep learning with texture-based approaches. This method has applications in OCR systems and assistive technologies for the visually impaired.
Copyright (c) 2025 Satishkumar Mallappa, Chandrashekar Gudada, P. Megana Santhoshi, Raghavendra (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning for Skin Disease Classification: A Comparative Study of CNN and CNN-LSTM Architectures
Skin diseases, particularly melanoma and other types of pigmented lesions, constitute a significant portion of global health concerns due to their prevalence and potential severity. In recent years, deep learning (DL) has revolutionized image classification tasks in the medical domain, particularly using Convolutional Neural Networks (CNNs) for skin lesion analysis. However, traditional CNNs are limited to capturing spatial features, often overlooking sequential patterns and complex contextual cues inherent in dermatological imagery. This study explores the automated classification of pigmented skin lesions using the HAM10000 dataset, a diverse collection of 10,015 dermatoscopic images spanning seven diagnostic categories. Addressing challenges in computational dermatology, we leverage MobileNet-V2 and InceptionV3 deep learning architectures, optimized via transfer learning and advanced preprocessing techniques. Comparative evaluation is performed between baseline CNN models and their Long Short-Term Memory (LSTM)-augmented variants to assess improvements in classification performance through sequential feature modeling. Results indicate that LSTM integration enhances contextual feature learning, improving accuracy for underrepresented lesion classes, with InceptionV3+LSTM achieving the highest classification accuracy.
Copyright (c) 2025 Fatmir Basholli, Mohammed R. Hayal, Ebrahim E. Elsayed, Davron Aslonqulovich Juraev (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence in Carbon Trading: Enhancing Market Efficiency and Risk Management
Carbon trading is a market-based technique to decrease greenhouse gas (GHG) emissions through the sale and purchase of carbon offsets. Incorporating artificial intelligence (AI) into carbon trading can alter the industry by improving information processing, statistical modeling, and trade automation. This paper presents an extensive structure for AI-driven carbon trading that considers critical aspects such as carbon trading volume and pricing to maximize productivity and sustainability. The study assesses numerous AI and machine learning (ML) theories, including their use in cost prediction, real-time market forecasting, and financial risk assessment. The main results show that AI integration increases market transparency, lowers fraud, and promotes informed decision-making, all of which helps to establish an environmentally friendly, effective, and adaptable carbon market. Furthermore, this work underscores the role of AI in advancing carbon-neutral economies by fostering innovation in emissions monitoring and reporting. These advancements highlight AI's critical contribution to achieving global climate objectives and addressing the urgent challenges posed by climate change.
Copyright (c) 2025 Mohammad Parhamfar, Aykut Fatih Güven, Anna Pinnarelli, Pasquale Vizza, Alireza Soleimani (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Efficient Medicine Demand Prediction System Using LTH-SES-Based Machine Learning Technique with Pharmacy Supply Chain
The Pharmaceutical Industry (PI) is deemed as one amongst the most substantial industrial sectors. Therefore, for the healthcare system, effective management of the Pharmaceutical Supply Chain (PSC) is crucial. Thus, an efficient medicine demand prediction system is proposed in this paper by using the Logistic Tanh activation adapted Single Exponential Smoothing (LTH-SES)-based Machine Learning (ML) technique with PSC. The previous medication sales data from the PSC are collected from publicly available sources to predict future medication needs. Later, to reduce the vast amount of information present in the dataset, the MapReduce model is performed. Later, the features are extracted. After that, by using the Renyi Entropy Principal Component Analysis (REPCA) technique, the dimensionality is reduced. Later, by using Min-Max Distance Centroid Fuzzy C-Means (M2DCFCM) clustering, the medications are grouped together. Lastly, for forecasting the future demand for medications, the LTH-SES technique is used. The proposed system’s performance is further validated and the outcomes exhibited that the proposed methodology forecasts the demand more effectively than other prevailing techniques.
Copyright (c) 2025 Guna Sekhar Sajja, Mohan Kumar Meesala (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Study of Join Algorithms in MySQL: Cross, Inner, Outer, and Self Joins
A Structured Query Language (SQL) joins are fundamental to relational database operations, merging data from multiple tables into coherent result sets. This comparative study examines the performance characteristics of cross joins, inner joins, left/right/full outer joins, and self-joins in MySQL under large-scale workloads. Experiments were conducted on datasets ranging from 10,000 to 50 million rows using execution time, CPU utilization, memory consumption, and scalability as metrics. Results demonstrate inner joins achieve superior performance when properly indexed; outer joins incur moderate overhead for handling unmatched rows; self-joins perform comparably to inner joins with effective indexing but degrade under multi-level recursion; cross joins exhibit exponential growth and should be used sparingly. Recommendations for index strategies and query design are provided.
Copyright (c) 2025 Adhuresa Hajdini, Lozarta Fazliu, Dea Gjoshi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0