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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
June 2025 | Vol. 103 No.12

Title:	IMPACT OF BROKEN LINKS AND DEAD CODES ON OPEN-SOURCE REPOSITORIES: AN AI AUTO ENCODER APPROACH FOR SENSITIVE DATA PROTECTION
Author:	M. MUTHALAGU , DR. R. RATHINASABAPATHY
Abstract:	This article examines the effects of broken links and obsolete code on open-source repositories' usefulness, security, and sustainability. Defunct links, sometimes resulting from outdated or deleted external resources, hinder developers' access to essential documentation, libraries, and tools. Dead code, denoting abandoned or old code inside repositories, complicates maintenance and exposes possible risks. Furthermore, the proliferation of big data introduces distinct issues in managing large volumes of unstructured sensitive information, especially regarding extraction and analysis. This paper suggests implementing a Deep Autoencoder (DAE) model for protecting sensitive data. This method utilizes AI-driven auto encoders to identify sensitive data patterns, encrypt them, and discover weak connections and obsolete code for effective elimination. The optimized DAE algorithm demonstrates enhanced performance with increased detection rates, diminished false positives, and minimized failure jitter, making it a reliable option for risk assessment and prolonging the lifespan of open-source repositories. The results underscore the need for consistent maintenance and community cooperation to enhance open-source software ecosystems' quality, dependability, and security.
Keywords:	Data Mining, Sensitive Data, DAE (Deep Autoencoder), Broken Links, Dead Code, Open-Source Repositories, Code Maintenance, Software Sustainability, Code Quality, Developer Collaboration, Repository Integrity.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	THE INFLUENCE OF SOCIAL MEDIA AND DIGITAL COMMUNICATION ON THE EVOLUTION OF VOCABULARY AND GRAMMATICAL STRUCTURES
Author:	VIKTORIIA SIKORSKA, OKSANA SNIGOVSKA, HANNA PEREDERII, ALINA АNDROSHCHUK, OLEKSANDR KALISHCHUK
Abstract:	The article discusses the impact of emerging communication technologies and social networks on the development of lexical and grammatical norms of the English language. The study is dedicated to the most important tendencies in language evolution, i.e., the emergence of neologisms, acronyms, abbreviations and borrowings, and grammatical simplifications and non-standard syntactic structures. Its importance is due to the need to investigate the mechanisms of language norm adaptation into the ever-changing digital environment, reshaping traditional language standards and communication methods. The research is based on the study of linguistic features of five popular sites (Twitter, Facebook, Instagram, TikTok, Reddit), which allows us to identify the specifics of the use of linguistic innovations in different situations of online communication. The article aims to determine the nature and causes of digital language changes, systematise their lexical and grammatical manifestations, and assess the impact of age and social factors on language dynamics. The study used a set of methods: content analysis, comparative and contrastive analysis, sociolinguistic approach, and descriptive analysis. The material was 250 text samples from five digital platforms. According to the research results, social networks are an effective mechanism for linguistic innovation, creating novel communication models and evolving forms of classical languages to digital ones. It has been established that different platforms have some linguistic features: Twitter is characterised by the active shortening of words and phrases, and TikTok and Instagram utilise non-standard grammatical forms with ironic or humorous connotations. Reddit is characterised by language play and violation of traditional syntactic rules. The research also revealed a strong dependency of language variations on users' age and social qualities: young people are the primary agents of language development. At the same time, their seniors keep traditional language norms. The findings may be used in future research on digital linguistics, namely how social media affects academic writing, professional jargon, and the long-term restructuring of the language system.
Keywords:	Social Networks, Language, Language Change, Online Communication, Communication, Language Tools
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	SIGNAL PROCESSING IN MANET SMART ANTENNAS
Author:	S.HEMALATHA, V.SEETHALAKASHMI, PULLELA SVVSR KUMAR, TAVANAM VENKATA RAO, DR.G. ANUREKHA, MS. B YAMINI SUPRIYA
Abstract:	Each layer of the Mobile Adhoc Network protocol stack has subject to some kind of issues, Majorly the lower three layers are facing the major challenges in packet transmission without falls on antenna usage, Hidden and Exposed nodes issues and routing strategy. Several research works was carried out individually addressing the layers to solve the problem, but still research exists on these three layers. This article considering the three layers to give better signal processing systems in MANET antennas. The proposed work consists of combines base three layers together in to single layer named as Antenna Beams Direction Based Routing Protocol which used the smart antenna to select the beam for the transmission with the support of hidden and exposed nodes table, finally this work provides the better packet transmission and proved the best result. The proposed work was simulated with the support of NS and the result compared with the Omni directional antenna with the parameter of SNR, Radiation Intensity, Directivity, was compared and the results proved that proposed work done the best in 20 % to 30 % performance in comparing with the existing protocol stack.
Keywords:	Antenna, Hidden and Exposed Terminal (HET) problem, MANET, Physical Layer, MAC layer, Routing protocol
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	ARTIFICIAL INTELLIGENCE AND HUMAN EXPERTISE IN CRIMINAL INVESTIGATIONS: INTEGRATING NLP TECHNIQUES TO ENHANCE JUSTICE
Author:	AWS I. ABUEID
Abstract:	This study explores AI's role in analyzing language interactions within investigative chambers and courts, using natural language processing (NLP) technologies such as Bert and GPT-4. The focus was on assessing questions posed and responses provided by witnesses or defendants, where AI was used to analyze emotional biases, inconsistencies in testimonies, and deliberate ambiguity in responses. The study draws on AI tools to detect violations of conversational principles, such as ambiguity and asymmetry, that may affect the integrity of investigations. The study results showed that AI can identify violations of conversational principles with an accuracy that exceeds human analysis by up to 30%, with GPT -4 and Roberta detecting violations such as intentional ambiguity (45%) and asymmetry (40%) in responses. AI has also shown a high ability to generate complex investigative questions that reflect legal and social context, such as questions intended to link testimonies with available evidence, enhancing analysis accuracy and reducing emotional biases that may affect human investigators. However, several challenges associated with the use of AI have been identified in this context, most notably algorithmic bias, where data biases used to train models may affect fairness and transparency in outcomes. The black box problem, which relates to the difficulty of interpreting how models make their decisions, raises questions about transparency and accountability in the judicial system. The study found that integrating AI with human expertise could improve criminal justice through hybrid judicial platforms, where AI is used to analyze raw data. In contrast, the human investigator remains responsible for interpreting and making final decisions. Based on these findings, recommendations were made to develop ethical frameworks to ensure the transparent and reliable use of AI in judicial investigations.
Keywords:	Artificial Intelligence, Natural Language Processing, Algorithmic Bias, Judicial Investigation, Criminal Investigations
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	A DEEP LEARNING-BASED HYBRID MODEL FOR AUTOMATED LUNG DISEASE DETECTION: ADDRESSING COVID-19 DIAGNOSTIC CHALLENGES
Author:	NIGHILA ASHOK K , S SIVAKUMARI
Abstract:	Lung abnormalities in the post-COVID era are a common issue, demanding accurate and timely diagnosis. However, detecting these irregularities with deep learning models has its own difficulties like class imbalance, overfitting, and restricted generalizability due to heterogeneous datasets, all of which can impede accurate detection. Here we apply deep learning classification on extensive datasets compiled during and after the COVID-19 pandemic, with an ability for high-performance AI algorithms to peruse normal lungs and lungs affected by pneumonia, cardiomegaly, and COVID-19. We implemented an integrated framework of two convolution neural network architectures, Places365 GoogLeNet and EfficientNetB0. The fusion of these models employed the AdaBoost ensemble method, significantly enhancing classification accuracy. Places 365 GoogLeNet achieved a validation accuracy of 90.49%, while EfficientNetB0 reached 94.70%. By integrating these models, the classification accuracy improved to 97.48%, showcasing the effectiveness of model fusion in achieving superior performance. This framework demonstrates promise for diagnosing complex lung conditions, particularly those related to COVID-19, offering potential as a robust diagnostic tool.
Keywords:	COVID-19, Places 365 GoogLeNet, CNN, EfficientNet B0, ResNet, Pneumonia, Cardiomegaly, Ensemble
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	A DEEP LEARNING-BASED HYBRID MODEL FOR AUTOMATICALLY DETECTING DEPRESSION IN SOCIAL MEDIA POSTS
Author:	T. SRAJAN KUMAR, DR. M. NARAYANAN, DR. HARIKRISHNA KAMATHAM
Abstract:	In recent times, mental health issues have been on the rise, influenced by various factors, including lifestyle changes. With the widespread use of social media, individuals from different backgrounds can openly share their thoughts and emotions, providing valuable data for research. This has opened the opportunity to analyse social media discussions to evaluate the potential presence of depression by examining the sentiments conveyed in the text. Although various heuristic methods for depression detection (DD) are available, the rise of AI has enabled the development of more efficient learning-based methods. However, since a single approach may not be universally applicable, there is a need to refine DL models to improve their performance in detecting depression. In this paper, we introduce a DL context that integrates CNN and BiLSTM networks, enabling the model to capture both features from the data and temporal dependencies. We present an algorithm called Learning Based Depression Detection (LBDD), which analyses Twitter posts to classify them based on the probability of depression. After evaluating the approach on a standard dataset, the projected model outclasses several prevailing methods, achieving an accuracy of 96.32%.
Keywords:	Deep Learning (DL), Depression Detection, Convolutional Neural Networks (CNN), Bi-Directional Long Short-Term Memory (Bilstm)
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	MULTI LEVEL ENCRYPTION CIPHERTEXT USING MULTI BLOCKS AND SECURE DATA ACCESS POLICY IN CLOUD COMPUTING
Author:	SAJJA KRISHNA KISHORE, DR. GUDIPATI MURALI, DR. PADMAJA PULICHERLA
Abstract:	An emerging paradigm in computing that aims to provide customers with the right solutions are cloud computing. Whether multi-layer authentication or multi factor verification makes accessing data stored in a cloud computing environment simple, it relies mostly on many current technologies such as virtualization, grid computing, etc. Results from the simulation show that three-tier authentication is more efficient than competing methods, lending credence to its viability as a cloud authentication solution. To enhance cloud safety and provide effective privacy security, a multi-level micro-access limitation formula is first suggested. Cloud data goes through many layers of indexing. Security is used to varying degrees during data transmission. The advantages listed in the configuration papers are used to assess the customer's requests for reliable accessibility. Data stored in the cloud is similarly encrypted using a key that is owned by the data owner. Both the efficacy and precision of cloud security and privacy protection are improved by this method.
Keywords:	Multi-level, Micro-Access, Privacy, Cloud Data, Security, Authentication, Trusted Access, Encrypted
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	ADAPTIVE SEA LION OPTIMIZED RESNET50 (SLIA-RN50) FOR ENHANCED MRI TUMOR CLASSIFICATION
Author:	S. SHANMUGAPRIYA, P. RUTRAVIGNESHWARAN
Abstract:	MRI-based tumor classification requires high precision, adaptability, and computational efficiency to ensure reliable diagnosis. Conventional deep learning models struggle with feature extraction, misclassification, and optimization inefficiencies, limiting their effectiveness in medical imaging. The purpose of this study is to enhance classification performance by integrating bio-inspired optimization with deep learning. A Sea Lion Inspired Adaptive ResNet50 (SLIA-RN50) framework is introduced to optimize hierarchical feature extraction, improve adaptability, and enhance decision-making in MRI tumor classification. The methodology involves leveraging sea lion-inspired strategies to refine multi-scale integration, collaborative filtering, and energy-efficient processing, ensuring balanced precision and recall. The proposed SLIA-RN50 model outperforms existing architectures in classification accuracy, precision, and F-measure, significantly reducing false positives and false negatives. Experimental results validate the effectiveness of the optimized framework, confirming its potential for automated MRI-based tumor detection. The bio-inspired model presents an efficient and scalable solution for improving computational techniques in medical image classification.
Keywords:	MRI Tumor Classification - ResNet50 - Sea Lion Optimization - Medical Image Processing - Feature Extraction
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	USING ARTIFICIAL INTELLIGENCE-BASED CHATBOTS FOR INTERACTIVE UKRAINIAN LANGUAGE LEARNING
Author:	HANNA STROHANOVA, OLENA STEPANENKO, ANZHELIKA POPOVYCH, OKSANA KRYMETS, LARYSA UDOVYCHENKO
Abstract:	The use of chatbots based on artificial intelligence (AI) for teaching the Ukrainian language was considered highly relevant in the context of the digitalization of education and the increasing demand to make students’ speech development more effective. Interactive tools were found to engage students more actively in the learning process, addressing the limitations of traditional methods that often failed to accommodate individual learners’ needs. The aim of the study was to evaluate the impact of AI-based chatbots on the development of speech competencies among secondary school students. The methodology included a questionnaire survey of students, a pedagogical experiment involving chatbot-assisted learning, comparative analysis of learning outcomes between the control group (CG) and the experimental group (EG), and systematic observation of speaking activity. The study confirmed that the use of chatbots significantly improved students’ speaking skills, increased their motivation, and enhanced lesson interactivity. Specifically, the EG students demonstrated a 27% improvement in productive speech performance and a 35% increase in the number of speech acts. In comparison, the CG showed only a 12% increase in productivity and a 9% rise in speech acts, which was notably lower than the EG results. The conclusions highlighted that the integration of chatbots contributed to the development of sustainable communicative skills and supported students’ motivation to learn. Teachers acknowledged the pedagogical potential of such tools but also emphasized the need for additional training and improved technical infrastructure. The academic novelty of the research lay in the development of an interactive methodology for teaching Ukrainian, which effectively combined traditional practices with innovative approaches such as personalization and gamification. The practical value consisted in the applicability of the results for improving Ukrainian language instruction, guiding the integration of AI tools into curricula, and enhancing teacher qualifications.
Keywords:	Language Competence, Adaptive Methods, Innovative Approaches, Educational Progress, Educational Interactivity, Digital Pedagogy, Chatbots, Ukrainian Language.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	RESILIENCE GREY WOLF OPTIMIZATION-BASED CONVOLUTIONAL NEURAL NETWORK (RGWO-CNN) FOR CLASSIFICATION OF HEART DISEASE AND DIABETES (HDD)
Author:	S.USHA , R.AMEEN SHERIFF , S.KANCHANA
Abstract:	Heart Disease and Diabetes (HDD) has reached epidemic proportions worldwide. Long-term issues affecting human organs are more likely due to HDD. If this critical medical condition is diagnosed at an early stage, people may be able to live healthier and longer lives by taking proper medications. The properly trained machine learning models on relevant datasets will help to diagnose HDD, but the significant issue is the lack of classification accuracy. Even more importantly, most current machine learning algorithms focus on predicting specific diseases. A classifier that accurately predicts the incidence of several diseases might be helpful in this context. This paper proposes a Resilience Grey Wolf Optimization-based Convolutional Neural Network (RGWO-CNN) to classify HDD. In the proposed RGWO-CNN method, the hyperparameters of the CNN model are represented as individual wolves in the RGWO algorithm. The position of each wolf represents a specific set of hyperparameters. The RGWO algorithm iteratively updates the position of each wolf based on their fitness (performance) and the wolves' social hierarchy (dominance). The updated position of each wolf corresponds to a new set of hyperparameters used to train and evaluate a new CNN model. This work uses RGWO-CNN to the Cardiovascular Disease Dataset and the PIMA Indian Diabetes Database to evaluate its performance. The assessment results reveal that the proposed classifier offers superior classification accuracy compared to the state-of-the-art classifiers.
Keywords:	Diabetes, Grey Wolf, Heart Disease, Neural Network, Optimization, Particle Swarm
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	INNOVATIVE IOT-BASED STRATEGY FOR WATER QUALITY CONTROL IN HYDROPONIC PLANTS USING MEDIAN FILTER AND LINEAR QUADRATIC ESTIMATION
Author:	CAHYA LUKITO, Rony Baskoro Lukito, Endang Ernawati
Abstract:	Controlling water quality in hydroponic farming is essential but challenging. IoT sensors help monitor water conditions, but the data they produce is often inaccurate and water quality control becomes ineffective. This research uses an innovative approach in remote monitoring and controlling water quality in hydroponic systems through the integration of Internet of Things (IoT) technology for real-time data collection and data processing algorithms. The proposed strategy is to use a Median Filter combined with Linear Quadratic Estimation to produce more precise water quality control for hydroponic plants. Median Filter effectively reduces the noise from data obtained from sensors, while Linear Quadratic Estimation is used to predict the state of water quality of hydroponic plans. Experimental results show that the proposed system achieves mean absolute error (MAE), and root mean square error (RMSE) values below 1% for both PPM and pH measurements. This indicates that sensor data can be effectively processed, and the estimation of water quality changes closely reflects the actual conditions of the water. The approach using these two methods can ensure that the water quality of hydroponic plants becomes more stable and controlled, thus having an impact on the fertility and health of the plants and increasing better yields.
Keywords:	Internet of Things, Hydroponics, Water Quality, Median Filter, Linear Quadratic Estimation
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	CLOUD IOT ENVIRONMENTS SECURITY: DEEP LEARNING WITH GENERATIVE AND EXPLAINABLE MODELS
Author:	JYOTHI ANANTULA, VENKATA KRISHNA RAO LIKKI, RATHNA JYOTHI CHADUVULA, SHAHEDA NILOUFER, KONALA PADMAVATHI, PADMAVATHI PANGULURI, J. USHA KRANTI
Abstract:	IoT devices have grown exponentially, and businesses are utilizing cloud computing to integrate complex applications with those devices, which poses excellent security concerns regarding confidentiality, integrity, and availability of sensitive data. This paper offers a security framework to mitigate against the threats above utilizing deep learning, Generative Adversarial Networks (GANs), and Explainable AI (XAI). To alleviate the challenges of anomaly detection, especially for rare and novel attacks, the proposed framework uses GANs (Generative Adversarial Networks) to produce synthetic data. XAI methods such as SHAP and LIME have been established to bring more transparency and trust to models, which is incredibly important for security professionals. The framework also integrates federated learning, where models can be trained across decentralized devices while keeping data private. The experimental results demonstrate that our proposed model can achieve achieving94.8% accuracy94.6%precision97.1%recallon NSL-KDD datasets, which are lower than other models Lowest Latency of 45ms per sample. The results validate the model for scalable, interpretable, and privacy-preserving real-time Internet of Things (IoT) security applications.
Keywords:	Cloud Computing, IoT Security, Deep Learning, Generative Models, Explainable AI, Anomaly Detection, Trust, Privacy, GANs
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	EVALUATING PREDICTIVE CERTAINTY IN AI MODELS FOR ACCURATE BRAIN TUMOR DETECTION
Author:	GOKAPAY DILIP KUMAR, SHAIK THASEENTAJ, KUNDA SURESH BABU, JELDI ASHA JYOTHI, NUTHALAPATI KAMALA VIKASINI, PREM SWARUP MALLIPUDI
Abstract:	Brain tumors are highly dangerous and often life-threatening, significantly affecting patients’ overall health and quality of life. This research explores prediction certainty, an underrepresented area, as existing research focuses on accuracy. This study highlights establishing a correlation between a model’s loss value and greater certainty in predictions. Along with conventional performance metrics such as precision and recall, this work emphasizes the critical role of loss value. To assess the reliability and effectiveness of artificial intelligence models, including CNN, ResNet-50, XceptionNet, and a proposed model (integrating advanced layers), were tested. The study prioritized loss values for accurate detection of tumor cases, minimizing false negatives. The models are effective for real-time tumor detection due to their low loss values and efficient runtimes. Experimental results showed the following metrics on testing: CNN achieved a loss of 0.35 and 68.60% accuracy; ResNet-50 achieved improved performance with a loss of 0.17; and the proposed model achieved 90% accuracy with superior recall and runtime. The study concludes that while accuracy is important, the certainty in predictions plays a significant role in reliable tumor diagnosis. Given the global shortage of specialized medical professionals, the proposed approach addresses this gap by providing timely and accurate cancer detection tools, contributing effectively to healthcare systems and medical education, enabling future AI applications to be used effectively in clinical practice.
Keywords:	Brain Tumor Detection, Artificial Intelligence, Cnn, Resnet-50, Xceptionnet.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	TECHNOLOGY ACCEPTANCE MODEL ON PEDAGOGICAL SKILLS: THE MODERATING ROLE OF ANDROID GAME ADDICTION
Author:	ADE DWI CAHYANTI, FEBRIKA YOGIE HERMANTO , BRILLIAN ROSY, PRISILIA JOYCELINE ATMOJO, ANISA TRI UTAMI, MASDANIA SAYYIDATUL MAULIDA
Abstract:	This study was to determine the direct effect of the technology acceptance model on the pedagogical skills of prospective teachers and the moderating role of android game addiction on the relationship between technology acceptance model and pedagogical skills in teacher-candidate students. This study used Structural Equation Model (SEM) – by the Partial Least Square (PLS) analysis method, where the analysis is conducted by using the Smart-PLS 3.0. Respondents in this study were teacher-candidate students with the main criteria of having an addiction to playing android-based games. The findings indicate that, first, perceived ease of use of technology in learning can increase students’ pedagogical skills, where students can use technology effectively then they can reflect the knowledge to teaching skill of prospective teacher. Second, perceived usefulness does not affect pedagogical skills directly because there is more important role that affect students’ pedagogical skills, such as: students' motivation to learn and teach. Third, attitude toward using of technology in learning can increase pedagogical skills. The higher attention in using technology, the more increasing pedagogical skills. Fourth, android game addiction does not have mediating effect between perceived ease of use, perceived usefulness, and attitude toward using and pedagogical skills. This finding also shows that students' level of android games addiction has no significant relationship to academic performance, in the context of pedagogical skills. The findings also highlight the importance of social and emotional support in enhancing pedagogical skills, and suggesting that internal motivation play an important role.
Keywords:	Pedagogical Skills, Technology Acceptance Model (TAM), Android Game Addiction, Teacher Candidate Students
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	APPLICATIONS OF QUANTUM COMPUTING IN BIOMETRIC INFORMATION SECURITY
Author:	GALIYA YESMAGAMBETOVA, ALIMBUBI AKTAYEVA, KYMBAT SAGINBAYEVA, DENIS PLESKACHEV, ULZHAN KUSSAINOVA, AIDYN DAURENOVA, ISKANDER BAIZHANON7, ALTYNBEK UMBETOV
Abstract:	Quantum computing is based on the principles of quantum mechanics and uses quantum bits (qubits) that can exist in a state of superposition and entanglement. They promise a revolution in computing and information security. Firstly, using quantum algorithms in information technology opens up new possibilities for processing and protecting data. Secondly, biometric information security relies on a person's unique physical and behavioural characteristics, such as fingerprints, facial recognition, iris, voice, and others. These methods provide a high level of security due to their uniqueness and difficulty in counterfeiting. Combining the principles of quantum computing and biometric information security allows us to identify their similarities and integration capabilities to create reliable systems for protecting confidential information. While previous research in biometric information security has focused on individual aspects of decision-making to ensure the information security of educational institutions when using facial recognition technologies, this work presents a comprehensive framework combining several components into a single unified system for using various quantum cryptography algorithms. The main advantages of using quantum computing in object and image detection include accelerating the computational process using quantum components, robustness at different object angles, suitability for moving or static conditions, cryptographic noise immunity, and the ability to work in real practical conditions. The advantages of the quantum cryptographic scheme for face recognition technologies include ease of implementation, high cryptography security, the ability to parallelise the processes of encoding and decoding images, and the fact that complex computer equipment is not required. The study results provide a solid foundation for developing hybrid quantum computing and biometric information security technologies. Using the possibility of hybrid computing opens up new horizons for the standardisation and public dissemination of new technologies, and such a combination ensures reliable protection of confidential information and stimulates further research aimed at improving data protection methods in the context of the rapid development of the digital world.
Keywords:	Qubit, pattern recognition, quantum algorithm, multi-modal biometric technology, cybersecurity.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	OPTIMIZING REAL TIME IOT PROCESSING WITH HYBRID EDGE CLOUD ARCHITECTURE FOR ENHANCED LATENCY AND ENERGY EFFICIENCY
Author:	ANIL KUMAR PALLIKONDA, ANIL KUMAR KATRAGADDA, JAGADEESWARA RAO ANNAM, V.V.RAMA KRISHNA, SAI SIRISHA CHITTINENI, ESWAR PATNALA, VIPPARLA ARUNA
Abstract:	The proposed hybrid edge-cloud architecture system aims to maximize efficiency in dynamically processing real-time data for Internet of Things (IoT) powered applications. By strategically placing a fog layer, the system seeks to balance the load, ensuring efficient data processing while minimizing latency, optimizing energy consumption, and enhancing scalability. This is achieved through a Dynamic Task Offloading (DTA) algorithm that intelligently assigns tasks to either the edge or cloud layer. Experiments using a synthetic smart city traffic dataset demonstrate that the hybrid model can reduce latency by 70% and save 55% in energy consumption compared to a cloud-only model, while achieving a task offloading efficiency of 92%. The architecture supports high scalability, resource utilization, and timely decision-making, significantly reducing processing latencies and energy usage for IoT applications. The study highlights the limitations of cloud-only systems, including high latency and scalability issues, and addresses these by proposing a hybrid solution that enhances real-time IoT data processing capabilities in dynamic environments.
Keywords:	Hybrid Architecture, Edge Computing, Cloud Computing, IoT, Task Offloading, Latency Optimization
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	EDGE DETECTION IN MRI BRAIN TUMOR IMAGES USING MODIFIED ACO ALGORITHM BASED ON WEIGHTED HEURISTICS
Author:	BHARATI CHAUDHARI , AVINASH GULVE
Abstract:	Images play a crucial role in the medical field across various aspects, from diagnosis to treatment planning. The Magnetic Resonance Imaging (MRI) brain tumor images are always associated with noise. Therefore, to diagnose diseases, image edge detection plays a challenging role in the medical field. It identifies edges, boundaries, disruptions, irregularities, and other valuable features. Ant colony optimization (ACO) is a well-known metaheuristic algorithm inspired by the way ants lay down a chemical pheromone while searching for food. It generates a pheromone matrix, which provides edge information accessible at every pixel of the image, developed by ants navigating across the image. The movements of ants depend on the local variance of the image intensity value. Conventional statistical range-based approaches are limited in accurately identifying weak edges. The proposed approach enhances edge detection using ACO, integrating weighted statistical range-based heuristics information and Gaussian gradients to generate a binary image that enables to detect the strong edges. Thus by assigning weights to the neighborhood range of pixels helps to determine the direction in which ants are able to move. The Gauss gradient produces the edges effectively. The proposed method was tested with standard MRI brain tumor images. Experimental results exhibit the comparable performance of the proposed method with conventional edge detectors in terms of performance parameters like Figure of Merit, Sensitivity, Accuracy, and output images.
Keywords:	Ant Colony Optimization, Statistical Range, Edge Detection, Brain Tumor, Weighted Heuristics.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	COMPARATIVE ANALYSIS OF CLASSICAL AND QUANTUM ALGORITHMS FOR SOLVING LINEAR SYSTEMS OF EQUATIONS: THEORETICAL INSIGHTS AND BENCHMARKING CHALLENGES
Author:	DR.SRISUDHA GARUGU, DR. V. RAVI KUMAR, D.ASHWINI, PETHOTA SWAROOPA, K.V.D.S. SANTHOSH
Abstract:	Modern computers utilize a model based on a simple Turing machine concept. This study contains an extensive comparative review of classical and quantum algorithm approaches to solving a system of linear equations. The study details standard classical approaches like Gaussian Elimination or Conjugate Gradient Method and compares these with the algorithm of quantum theory algorithm HHL (Harrow-Hassidim-Lloyd). It considers the logic behind each of them, their benchmarks, scalability, implementation difficulties and practical use. Benchmarking results illustrate the fact that a classical approach continues to do especially good for small to moderate sized problems. However, quantum computation has the potential for an exponential speedup using algorithms like HHL in well-conditioned sparse matrices. The current limits of the hardware are discussed and future research directions needed to be able to utilize quantum computing for larger scale linear algebra problems are presented.
Keywords:	Quantum Computation, Linear Algebra, Computational Complexity, Quantum Computing Scalability, HHL Algorithm
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	CONSUMER PERCEPTION LINKED NOVEL SUPPLY-CHAIN MANAGEMENT (NCP-NSCM) ALGORITHM
Author:	DEVENDRA NATH PATHAK, RAKESH KUMAR YADAV, HITENDRA SINGH
Abstract:	Consumer sentiment has emerged as a central driver of supply chain management (SCM) in the modern competitive and dynamic market conditions. Conventional SCM systems hardly incorporate real-time consumer sentiment, resulting in inefficiencies, misplaced production, and lower customer satisfaction. While big data analytics (BDA) has been increasingly used to improve SCM operations, the majority of current models do not account for direct incorporation of consumer sentiment. Filling this gap, the present study introduces the Novel Consumer Perception-linked Supply-Chain Management (NCP-NSCM) algorithm. This algorithm formally integrates consumer perception information into supply chain decision-making to improve responsiveness, flexibility, and consumer focus. In contrast to earlier approaches, the NCP-NSCM framework produces improved demand forecasting, risk detection, and operational responsiveness by means of integrated sentiment analysis and SCM modeling. The effectiveness of the strategy is proven through performance measures like accuracy, precision, recall, and major operational gains such as lower lead time, cost reductions, and better order accuracy. The research presents new frontiers for supply chain design that dynamically synchronizes with changing customer expectations.
Keywords:	Big Data Analytics (Bda), Supply Chain Management (Scm), Procurement Planning, Demand Forecasting, Real-Time Monitoring.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	SECURE BROADCAST COMMUNICATION IN SENSOR NETWORKS: FORTIFYING THE KD AUTHENTICATION PROTOCOL
Author:	GURUPRAKASH B1,RAJALAKSHMI J , ANGEL HEPZIBAH R, NAZRIN SALMA S, MARIAPPAN E, RAMNATH M
Abstract:	Wireless Sensor Networks (WSNs) are self-organizing networks composed of sensor nodes deployed in potentially hostile environments, making them highly susceptible to various security threats. Among these, Broadcast Service Attacks pose significant risks by injecting invalid packets through compromised nodes, leading to excessive power consumption, memory overload, bandwidth variation, and disruption of reliable data aggregation. Ensuring secure broadcast authentication in WSNs is a critical and complex challenge. This research focuses on mitigating Broadcast Service Attacks using the Kurosawa-Desmedt (KD) authentication scheme. The proposed approach is evaluated and compared against the Feige-Fiat-Shamir (FFS) security algorithm using the NS-2 simulator. Simulation results demonstrate that the KD algorithm outperforms the FFS approach in terms of Packet Delivery Ratio, End-to-End Delay, Detection Accuracy, and Average Energy Consumption. The practical applicability and benefits of the proposed method are also briefly discussed. Furthermore, the application of this work has been discussed briefly in this paper.
Keywords:	Broadcast Service Attack, Predictive Hash Based Broadcast Protocol, Fiege Fiat Shamir Security Algorithm, Kurosawa-Desmedt Algorithm, Health Care Monitoring.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	THE ROLE OF DIGITAL PAYMENT ADOPTION IN MEDIATING THE EFFECT OF FINANCIAL LITERACY, PERFORMANCE EXPECTANCY, EFFORT EXPECTANCY, AND DIGITAL LITERACY ON SMES PERFORMANCE
Author:	FATIMAH AL MUNAWAROH, RINDANG WIDURI
Abstract:	This study examines the mediating role of digital payment adoption in the relationship between financial literacy, performance expectancy, effort expectancy, and digital literacy on the performance of small and medium-sized enterprises (SMEs). By integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) and financial literacy frameworks, this research provides empirical insights into how SMEs leverage digital payment systems to enhance financial management and business sustainability in Indonesia. Using Structural Equation Model Partial Least Squares (SEM-PLS), data were collected from 240 SMEs in Kuningan, West Java. The results indicate that digital literacy and performance expectancy significantly influence the adoption of digital payments, which in turn enhances SMEs performance. However, effort expectancy and financial literacy do not have a significant impact on digital payment adoption. Moreover, digital payment adoption is found to mediate the relationship between digital literacy and performance expectancy on SMEs performance. These findings highlight the importance of digital literacy and perceived usefulness in driving SMEs digitalization. Therefore, policymakers and financial institutions should focus on enhancing digital literacy programs and promoting the benefits of digital payments to accelerate SMEs adoption. Future research should expand the geographical scope and consider external factors such as government regulations and technological infrastructure.
Keywords:	Digital Payment, The Effect Of Financial Literacy, Performance EXPECTANCY, Effort EXPECTANCY, Digital Literacy Smes Performance
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	EFFICIENT UNDERWATER IMAGE CLASSIFICATION WITH ENHANCED YOLOV5 AND BEARDED DRAGON OPTIMIZATION (BeardYOLO)
Author:	SARAVANAN P , VADIVAZHAGAN K
Abstract:	The proposed work focuses on the enhancement of the EY5 model through the integration of BDO for image identification and classification for underwater scenes. The proposed methodology, developed for such an application, modifies the cell size in EY5, which prepares the model for applying BDO for higher speed and accuracy. The BDO algorithm in the EY5 comprises initialization, moving simulation, foraging, communication and cooperation, pursuit of prey, adaptation, learning procedures, and the termination procedure. A number of experiments were performed to evaluate the potentials of the developed optimized EY5 against competitors such as DeepSeaNet and MCANet. A true positive rate of 61.11% with a true negative rate of 58.82%, precision at 70% and has high overall classification accuracy. The improvement of the EY5 model is called as BeardYOLO Here, it shows more efficiency in terms of clustering and classifying the underwater images and becomes one of the best solutions to work with underwater images. The study thereby raises the possibility of achieving superior performance in a specific field by integrating both the theories of advanced optimization with modern artificial neural networks.
Keywords:	Underwater Image Classification - Enhanced YOLOv5 - Bearded Dragon Optimization - BeardYOLO - Deep Learning - True Positive Rate - Precision - Neural Networks
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	ANCIENT TAMIL CHARACTER RECOGNITION USING OPTIMAL THRESHOLDING WITH RESNET-CAPSNET
Author:	VIDHYAVANI A, DR. T. MANORANJITHAM
Abstract:	Ancient Tamil character recognition (TCR) is complex due to its orientation, scale and writing style varying from person to person. Further, it is a challenging task for Epigraphers. Researchers for the ancient Tamil scripts and languages are relatively less when compared to other languages. When the inscriptions are on the stone wall, it is more complex to identify the characters. This research work focuses on the recognition of different Tamil characters using automated segmentation and classification techniques. Initially, the input images are pre-processed and segmented. Here, the segmentation process is carried out by Otsu thresholding. Then, the optimal threshold value is determined by the optimization adaptive salp swarm algorithm (ASSA). Finally, the Tamil characters are recognized by the DL (deep learning) model Residual capsule network (ResNet-CapsNet). The experiment is analyzed via real-time dataset and compared with the other deep learning models. Finally, the proposed ResNet-CapsNet achieved a better overall accuracy of 91.3% and specificity of 88.3% respectively and is suitable for ancient Tamil character recognition.
Keywords:	Ancient Tamil Character, Scripts, Adaptive Salp Swarm Algorithm, Inscriptions, Residual Capsule Network
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	SIW CAVITY LOADED WITH DUMBBELL SHAPED SLOT ANTENNA FOR WIDEBAND APPLICATIONS
Author:	K. VIDYA SAGAR, RAMPRASAD RAVULA, SATTI SUDHA MOHAN REDDY, PANGULURI VINODH BABU, 5KADIYALA SUDHAKAR, GUNTI SURENDRA, SURYA PRASADA RAO BORRA
Abstract:	Substrate Integrated Waveguide (SIW) technology is significant in the design and development of modern communication systems, telecommunications, aerospace, automotive healthcare, radar and in sensing technologies owing to its unique combination of performance, integration capabilities and cost-effectiveness. This paper is designed a microstrip patch antenna with applied SIW on dumbbell shaped antenna is focused to obtain high bandwidth and high gain and directivity with minimum reflection coefficient value. The dimensions 38mm28mm1.575mm (L×W×t) of the proposed antenna, excited with micro strip line feeding technique. The antenna is resonated at 15.27GHz and 15.47GHz frequencies. The VSWR values are 1.06 and 1.08 which represents the antenna is a good matching with the ideal value of 1.0. The low VSWR represents efficiency is increased significantly in the transmission line and reflected energy values are negligible. The bandwidth achieved is 1.38GHz. The gain achieved with the proposed antenna is 4.4dB. Directivity, electric field distribution and current distribution parameters are also considered to evaluate the performance of the antenna. The results extracted with the proposed antenna is compared with other existing slot antennas shows superior results. This antenna can be used for wide band communication applications specifically in the domain of aerospace and medical era.
Keywords:	Bandwidth, Current Distribution, Micro Strip Patch Antenna, Gain, Reflection Coefficient, Substrate Integrated Waveguide, VSWR And Voltage Distribution.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	FINE-TUNING A MODIFIED XCEPTION ARCHITECTURE FOR ENHANCED BREAST CANCER DETECTION IN MAMMOGRAMS: AN ANALYTICAL STUDY
Author:	SREEVIDYA NELLI, KEZIA RANI BADHITI
Abstract:	This research focuses on the adaptation and optimization of the Xception deep learning model for the detection of breast cancer using mammogram images, contributing to the field of precision medicine. Breast cancer, a leading cause of morbidity globally, benefits significantly from early and precise detection. Our study leverages transfer learning to modify the Xception architecture, introducing custom adjustments to its final layers to better capture the subtle signs of malignancy in mammograms. These modifications are aimed at improving the model's accuracy without compromising its specificity. The model was optimized using the Adam optimizer alongside the ReLU activation function, which helped in dynamic learning rate adjustment and enhanced feature detection from complex mammographic images. We assessed the model's performance through a comprehensive set of metrics including accuracy (90.42%), precision, recall, F1 score, ROC AUC score (0.9388), and Cohen's Kappa. These metrics collectively suggest a robust improvement over traditional detection methods, with a significant reduction in false positives and negatives. This research findings show that the modified Xception model not only meets but exceeds expectations in terms of diagnostic accuracy and reliability, offering a potent tool for clinicians. This research not only advances the application of deep learning in medical imaging but also paves the way for further research into AI-assisted diagnosis, potentially transforming clinical decision-making processes in breast cancer management. The implications of this work extend beyond immediate diagnostic benefits, supporting a broader shift towards more personalized and effective healthcare solutions.
Keywords:	Breast Cancer Detection, Mammogram Analysis, Deep Learning, Xception Model, Optimization Techniques, Medical Imaging
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	DECISION TREE BASED ON HIPPOPOTAMUS OPTIMIZATION ALGORITHM FOR SECURING IOT HEALTHCARE SYSTEMS
Author:	SURA MUSTFA ABBAS , RIYAM AMER WAHED , ELSAYED IBRAHIM ELSEDIMY , FADHIL ABD RASIN
Abstract:	This paper presents how the Internet of Things (IoT) has transformed healthcare by improving system efficiency and reducing the need for human intervention, while highlighting the security vulnerabilities arising from its interconnected nature. Intrusion Detection Systems (IDS) play a critical role in mitigating these threats, and recent advancements in machine learning and artificial intelligence have significantly enhanced detection capabilities. Among the various techniques, decision tree-based models have proven particularly effective in handling the large and complex data flows typical of IoT environments. To further improve security, the study incorporates advanced encryption methods and proposes the Hippopotamus Optimization Algorithm (HOA), which simulates the behavior of hippopotamuses to optimize IDS performance. By fine-tuning decision trees and other classifiers, HOA achieves higher classification accuracy and more efficient anomaly detection. Comparative analysis of machine learning algorithms—such as Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), K-Means clustering, and Random Forest—demonstrates that ensemble and deep learning models are more robust for securing IoT healthcare systems. Although models like Random Forest and KNN show high detection accuracy, challenges such as class imbalance remain. The proposed HOA-based hybrid model addresses these limitations by optimizing the precision-recall trade-off, ultimately providing a more resilient security framework for IoT-based healthcare applications.
Keywords:	Decision tree, Hippopotamus optimization algorithm, IoT healthcare systems.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	SECURING SMART GRIDS WITH DEEP CNNS IN NEXT GENERATION 5G-IOT ECOSYSTEMS
Author:	B V SUBBAYAMMA, K S MANI, B SWARUPA RANI, MANAM RAVINDRA, SUDHA SREE CHEKURI, VASAVI MANDADI, BHAGYA LAKSHMI NANDIPATI
Abstract:	The implementation of 5G networks and IoT devices in smart grid applications facilitates the electricity-generated, distributed, and managed bidirectional transfer of real-time information between utility suppliers and consumers. Nonetheless, this heightened transmission and assurance in IoT devices also introduce unprecedented security issues, as they are susceptible to malicious assaults. Implementing effective attack detection techniques in 5G-IoT smart grid systems for dependable and efficient power distribution, together with the prompt and precise identification of attacks, is essential. A novel technique, termed Target Projection Regressed Gradient Convolutional Neural Network (TPRGCNN), is developed to enhance the accuracy of attack detection in data transmission inside a 5G-IoT smart grid context. The TPRGCNN approach integrates feature selection and classification to enhance secure data transfer by identifying assaults in 5G-IoT smart grid networks. During the feature selection process, TPRGCNN employs the Ruzicka coefficient Dichotonic projection regression approach to improve attack detection accuracy while reducing time complexity. Subsequently, the chosen significant features are input into Jaspen’s correlative stochastic gradient convolutional neural learning classifier for the purpose of attack detection. Classification determines if the transmission is standard or indicative of an assault within the 5G-IoT smart grid network. The implementation findings indicate that the suggested TPRGCNN approach achieves a 5% enhancement in attack detection accuracy and a 2% increase in precision, recall, and F-score, while simultaneously reducing time complexity and space complexity by 13% and 23%, respectively, in comparison to existing methods.
Keywords:	5G-Iotattack Detection, Smart Grid, Ruzicka Coefficient, Convolutional Neural Classifier, Soft Step Activation
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	ENHANCING EARLY DETECTION AND INTERVENTION IN MENTAL HEALTH DISORDERS THROUGH MULTI-MODAL AI TECHNIQUES
Author:	LAKSHMIKANTH PALETI, KRISHNA ANNABOINA, ADUSUMILLI RAMANA LAKSHMI, RIAZ SHAIK, A S MALLESWARI, K SWATHI, RAMESH PETTELA
Abstract:	This study presents a novel multimodal AI framework for early detection of mental health disorders using speech and text analysis. The system employs a transformer-based text encoder, and a hybrid convolutional-recurrent speech encoder integrated via an attention-based fusion mechanism. This dynamic fusion approach in diagnostic accuracy outperforms traditional uni-modal and static fusion methods. Experiments on benchmark datasets yielded an accuracy of 89.5%, precision of 88.0%, recall of 90.2%, F1-score of 89.1%, and AUC of 0.93. Additionally, the framework supports proactive intervention by providing real-time clinical recommendations and enhancing treatment outcomes. The proposed method offers significant potential for improving early mental health diagnostics and intervention, contributing to the shift towards AI-enhanced mental health care.
Keywords:	Multi-modal Analysis, Mental Health Diagnostics, Early Detection, Speech Analysis, Text Analysis
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	MAPPING KNOWLEDGE: EVALUATING CONCEPT MAPS USING SOCIAL NETWORK ANALYSIS AND NETWORK SIMILARITY MEASURES
Author:	SUNU MARY ABRAHAM, G. SUDHAMATHY
Abstract:	Traditional multiple-choice questions (MCQs) primarily assess factual recall and application but often fail to capture the depth and structure of student understanding. Addressing this limitation, the present study introduces a novel framework that leverages Social Network Analysis (SNA) and network similarity measures to evaluate concept maps generated from students' MCQ responses. The framework enhances assessment by identifying key concepts and assigning weighted importance based on degree centrality and influential nodes. By comparing student-constructed concept maps with an instructor’s reference map, it assesses the coherence and completeness of student knowledge. The study also evaluates learning depth through both self-constructed and quiz-derived concept maps, offering insights into students’ conceptual development. Furthermore, it clusters students based on performance, uncovers learning patterns, and identifies weaker concepts, unknown concepts, and misconceptions. This integrated approach facilitates efficient and consistent assessment, enables personalized instruction, and supports targeted pedagogical interventions, ultimately contributing to improved learning outcomes and deeper knowledge acquisition.
Keywords:	Concept Map, Learning Analytics, Social Network Analysis, Influential nodes, Degree Centrality, Similarity Measures, Jaccard Similarity, Cosine Similarity.
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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Title:	AN EFFICIENT ADVANCED AUTHENTICATION SYSTEM WITH TWO SERVERS –AN ARTIFICIAL INTELLIGENCE SECURE SYSTEM
Author:	DR PRAVEEN KUMAR MANNEPALLI, P. NAGAMANI, DR. KOLLURU SURESH BABU, KALE NAGA VENKATA SRINIVAS, B SARITHA, DR P.MANIKYA PRASUNA, CH. CHANDRA MOHAN
Abstract:	The authentication server, which stores easily derived password verification data or plain text passwords in a central database, is completely trusted by the majority of password-based user authentication systems. As a result, these systems are not at all resistant to server-side offline dictionary assaults. An organization may face severe legal and financial consequences if the authentication server is compromised by either insiders or outsiders, exposing all user credentials. In order to get around the single point of vulnerability that comes with the single-server architecture, a number of multiserver password schemes have recently been developed. However, because either a user must communicate with numerous servers at once or the protocols are rather costly, these multiserver systems are challenging to implement and run in real-world scenarios. In this research, we propose a novel two-server architecture for a workable password-based user authentication and key exchange system. There are several attractive characteristics in our system. Our method may be immediately used to reinforce current single-server password systems because only a front-end service server interacts with users directly, while a control server operates in the background. Furthermore, neither of the two servers can launch offline dictionary attacks against the system.
Keywords:	Authentication, Two Server, Password, AI Driven, Efficient System
Source:	Journal of Theoretical and Applied Information Technology 30^th June 2025 -- Vol. 103. No. 12-- 2025
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