JANOLI International Journal of Computer Science and Engineering
JANOLI International Journal of Computer Science and Engineering (JIJCSE) is a distinguished publication in computer science and engineering, providing a platform for scientists and scholars engaged in various aspects of computer science and technology. We are committed to disseminating high-quality, peer-reviewed papers that showcase novel research and innovative applications. Our rigorous peer review process ensures the selected papers' originality, relevance, timeliness, and readability. While the primary focus is on previously unpublished material, exceptional conference presentations with outstanding merit may also be considered for publication, subject to meeting our peer review requirements. In addition to research articles, we welcome well-written survey and review articles from subject experts, fostering intelligent awareness of the latest technological trends and advancements. The journal adheres to a stringent double-blind peer review policy to maintain the good quality of research publication. With an acceptance rate of 10% of submitted manuscripts, the journal encourages recent discoveries and innovations comprising theoretical, conceptual and empirical research in management and applied research. Subject: Computer Science
Shanti Educational Research Foundation, India
The Role of Linear Algebra in Machine Learning Algorithms: A Review
Linear algebra forms the mathematical backbone of modern machine learning (ML). Concepts such as matrices, eigenvalues, eigenvectors, and vector spaces are essential for representing data, optimizing models, and enabling efficient computation. This review paper analyzes the role of linear algebraic structures in the design and functioning of ML algorithms. A systematic review methodology is adopted to examine their applications in data representation, dimensionality reduction, optimization, and neural networks. The study highlights that linear algebra not only improves computational efficiency but also enhances model interpretability and scalability.
Smartphone Brand Loyalty Prediction Using Behavioural and Preference Data
Knowing ahead of time whether a student will stick with their current smartphone brand or switch at their next upgrade is something marketers genuinely want to predict. This paper tackles exactly that question. We put together a full machine learning pipeline, ran it on survey data from 2,000 student respondents, and went head-to-head with four classifiers: Logistic Regression, Random Forest, Gradient Boosting, and Extra Trees. The pipeline was built with one particular concern in mind data leakage which is a surprisingly common mistake in survey-based classification studies and tends to produce accuracy numbers that look great on paper but fall apart in practice. Eight behavioural and attitudinal features served as the model inputs, each encoded according to its type. Gradient Boosting won the comparison, reaching 90% test accuracy with an F1-score of 0.9180 and a ROC-AUC of 0.9535. Worth noting: hyperparameter tuning actually made things slightly worse, which tells us that the defaults were already near the sweet spot for this particular dataset. A Streamlit web interface was also developed so that people who do not write code can still run predictions and explore results on their own.
brand loyalty, machine learning, binary classification, gradient boosting, feature engineering, consumer behaviour, smartphone, scikit-learn, Streamlit
Analysis of Consumer Behaviour in Food Delivery Platforms
During recent years online food delivery applications like Zomato, Swiggy and others became a routine for many people. Nowadays, it is even easier and more comfortable for the young generation like students ordering food online instead of going out and dining somewhere. Therefore, there has been a generation of a lot of information about people and their behaviour that can help to find patterns and understand the target audience better. To avoid using someone else's dataset, we developed and distributed our own Google form and obtained 462 responses from actual customers. Thus, we had a chance to get behavioural data and see actual trends in our field. We started working on the task by performing preprocessing and analysing of the received data using Python. We managed to visualize various trends related to expenditure per order, ordering frequency, time, platform preference, etc. Moreover, we developed a machine learning algorithm using Decision Tree classification. As a result, we found that people preferred moderate expenditures, ordered mostly at night time, and preferred to use Zomato service. Our predictive model reached only approximately 21.7% accuracy and proved the complex nature of humans' decision-making process.
Online Food Delivery Consumer Behavior Data Analysis Decision Tree Classification
Object Detection and Classification of Videos for Indexing &Retrieval Using Machine Learning
In today’s world, people have access to a huge amount of videos, both on the internet and television. But the amount of video data is large and it is increasing day by day as the technologies grow. So video data storage, classification for indexing, filtering,, and retrieval is a big issues. So it is very difficult for a human being to go through all the videos to search for a particular video and it is time-consuming, but the user wants a particular category of video or video genre within a short span. So that there is a need to classify the videos based on their genre, and subgenre, research has begun on automatically classifying videos. Many works has been done for the classification of videos in certain categories or genre and sub-genre, by filling the semantic between low level features of video and high level concepts, so that user can find their specific interest of video within a narrow domain. There are different techniques have been developed for good understanding of video content and various video features have been recognized for best representation of videos. There are different techniques have been developed for a good understanding of video content and various video features have been recognized for the best representation of videos. Such as Gaussian Mixture Model (GMM), Neural Network (NN), Bayesian, Hidden Markov Model (HMM), and Support Vector Machine (SVM). Object-based video classification method has been adopted in our work. The video classification system can be roughly divided into two major components: a module for extracting video features from key frames and another module to find feature similarities between video frames and the object from the database. Here the proposed project work is to classify the videos from the repository by the method of extracting regions of interest from each key frame of an input video clip. Then these regions of interest kept as features, are compared with features of objects for video recognition using the MSER algorithm. Finally, we retrieve the videos from the repository by understanding the constrained user text query is presented. The method is evaluated by experimentation over a dataset containing different types of videos i.e. cricket, football, cartoon, news, and movies.
Evaluating the Performance, Accuracy, and Reliability of a Computational Model for Automated Document Classification in Mental Health Diagnosis
The increasing prevalence of mental health disorders demands innovative computational tools to assist clinicians in diagnosis and monitoring. This study evaluates the performance, accuracy, and reliability of a proposed document classification framework for mental health diagnosis based on machine learning and natural language processing (NLP). Using a benchmark dataset of mental health reports, the study assesses the model across key metrics accuracy, recall, and precision and identifies limitations for future enhancement. Results indicate that Support Vector Machine (SVM) and Neural Network models outperform conventional classifiers such as Naïve Bayes and Random Forest in terms of diagnostic precision and generalization capability. Recommendations for improving dataset diversity, feature extraction, and interpretability are proposed.
Blockchain-Secured Federated Learning Approach for Adaptive Intrusion Detection in IoT Networks
The widespread adoption of Internet of Things (IoT) devices has expanded the attack surface, exposing networks to an increasing range of cyber threats. Conventional intrusion detection systems (IDS) struggle to cope with the dynamic, heterogeneous nature of IoT environments and often rely on centralized data processing, which raises privacy risks. This study presents an adaptive intrusion detection system (A-IDS) that integrates federated learning (FL) with blockchain-based trust management to strengthen IoT network security. Using FL, multiple IoT devices collaboratively train a global detection model without sharing raw data, ensuring privacy preservation. The blockchain component establishes a decentralized trust framework that safeguards the FL process and prevents malicious contributions. Performance evaluation through realistic IoT network simulations demonstrates high detection accuracy, strong privacy protection, and resilience to adversarial behavior. Comparisons with centralized and decentralized IDS approaches highlight the proposed system’s advantages in accuracy, robustness, and data confidentiality.
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Hybrid Deep Learning and Feature Engineering-Driven Adaptive IDS for Strengthening IoT Network Cybersecurity
The rapid expansion of Internet of Things (IoT) deployments has greatly increased the potential attack surface, exposing these networks to a wide spectrum of cyber threats. Conventional intrusion detection systems (IDSs) often face difficulties in accurately identifying advanced attacks within the diverse and dynamic IoT environment. This study presents an adaptive IDS that integrates hybrid deep learning with feature engineering to improve IoT network security. The framework applies feature engineering to derive discriminative and relevant attributes from network traffic, which are then processed by a hybrid model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The CNN autonomously learns hierarchical representations from the processed data, while the LSTM captures sequential dependencies, enabling effective detection of intricate and evolving threat patterns. Evaluation on a publicly available IoT traffic dataset reveals that the proposed approach outperforms existing IDS solutions in terms of detection accuracy, precision, recall, and F1-score. Moreover, its adaptive capability allows for dynamic parameter tuning and feature selection in response to changing threat landscapes, ensuring consistent and reliable defense for IoT infrastructures.
Dynamic Deception Techniques to Strengthen Cyber Defenses Against Advanced Persistent Threats
Advanced Persistent Threats (APTs) pose a significant and evolving challenge to modern cybersecurity. Traditional defense mechanisms often prove insufficient against their sophisticated techniques and patient persistence. This paper explores the application of adaptive cyber deception strategies to enhance cyber resilience against APTs. We propose a novel framework that dynamically adjusts deception tactics based on real-time threat intelligence, attacker behavior, and system vulnerability analysis. This framework leverages honeypots, honeynets, and decoy data strategically deployed throughout the network to detect, analyze, and disrupt APT activities. We present a detailed methodology for implementing and evaluating these adaptive deception strategies, including algorithms for deception selection, deployment, and maintenance. The results demonstrate a significant improvement in early threat detection, reduced attacker dwell time, and enhanced overall cyber resilience compared to static deception approaches. The research contributes to a more proactive and dynamic approach to cybersecurity, enabling organizations to better defend against the persistent and evolving threat posed by APTs.
Federated Learning-Driven Intrusion Detection with Blockchain-Backed Trust Frameworks to Strengthen IoT Network Security
The swift expansion of Internet of Things (IoT) ecosystems greatly enlarged the attack surface potential, rendering traditional centralized intrusion detection systems (IDS) progressively inadequate for effective network defense. This paper presents an adaptive IDS framework that combines federated learning (FL) with blockchain trust management to enhance IoT network security. In the envisioned design, IoT devices cooperatively build a shared intrusion detection model without sharing raw data directly, ensuring user privacy and bandwidth saving. A blockchain layer is leveraged to establish a distributed trust protocol for transparent verification and logging of contributions from each device to the FL process. Experimental tests, incorporating simulation and field deployment, demonstrate that the system is capable of successfully identifying varied IoT-targeted attacks at low false-positive rates. The results highlight the efficiency of the introduced approach in improving IoT security, while at the same time overcoming privacy, scalability, and trust challenges.
Adaptive Intrusion Detection System (A-IDS) for IoT Networks: A Hybrid Approach Leveraging Federated Learning and Edge Computing
The explosion of Internet of Things (IoT) devices has opened up an enormous attack surface, leaving such networks extremely susceptible to cyberattacks. Conventional Intrusion Detection Systems (IDS) tend to have problems handling the resource limitation of IoT devices, the nature of IoT traffic being dynamic in nature, and real-time detection of threats. This paper introduces a new Adaptive Intrusion Detection System (A-IDS) with a focus on IoT networks. A-IDS uses a hybrid method that integrates federated learning (FL) and edge computing to provide distributed, adaptive, and efficient intrusion detection. Local anomaly detection is carried out on edge devices with lightweight machine learning models that are collaboratively trained through FL. This reduces latency and saves bandwidth. A central server collects and fine-tunes the global model so the system can learn from dynamic threats. The recommended A-IDS is tested with a simulated IoT network with realistic traffic generation and attack models. The experiments show that A-IDS attains high accuracy detection, low rates of false positives, and low resource usage relative to conventional IDS methods. The research shows that FL and edge computing have the potential for improving the security of IoT networks by providing adaptive and distributed intrusion detection.
Emerging Trends in Cybersecurity: Innovations in Safeguarding Data Against Advanced Threats
This paper examines the emerging trends in cybersecurity through a review of how new strategies, such as artificial intelligence, blockchain, zero trust architecture, and quantum encryption, are revolutionizing data protection against evolving threats. The study has a qualitative methodology that comprises expert interviews and case studies to discuss the efficiency of these technologies. Key takeaways are the advancement in AI algorithms for real-time threat detection, scalable hybrid blockchain solutions, phased adoption strategies for zero trust architecture, and potential applications of quantum encryption. The survey also identifies the integration challenges that emerging technologies face in the existing system. Practical insights and adaptive frameworks are proposed that outline key areas in addressing these barriers and can serve as guidance for organizations to strengthen their cybersecurity infrastructure.
Securing the Future: Cybersecurity Strategies for Safeguarding Internet of Things Devices
The critical nature in which this study delves into enhanced cybersecurity measures for protection against more sophisticated cyber threats that victimize IoT devices is well informed by a qualitative approach to expert interviews and case studies. The paper addresses five sub-research areas, namely: the unique security challenges facing IoT, the role of encryption, the impact of user awareness, legal frameworks, and emerging trends in cybersecurity. Findings underpin the current security protocols' inadequacies, uneven deployment of encryption, and gaps in user education and regulatory enforcement. The study has pointed out the promise of AI-driven autonomous security systems and underscored the need for global cooperation and scalable solutions. Practical recommendations aim to bridge existing gaps to ensure better protection for IoT environments.
Enhancing Hospital Operations and Patient Care: The Role of AI in Smart Healthcare Systems
This paper explores the growing threat of ransomware attacks against businesses and sets forth ways to put effective defensive measures in place. Qualitative research, through case studies and interviews of experts, allows the researcher to examine critical aspects of ransomware defense, which include identifying vulnerabilities, advanced preventive measures, training of employees, dynamic incident response, and legal and ethical considerations. Findings underscore the need for proactive vulnerability management, strategic investment in AI-driven security technologies, continuous employee education, and dynamic incident response planning. The research emphasizes ethical considerations in ransomware negotiations and provides actionable recommendations to enhance organizational resilience against such threats
AI-Driven Cybersecurity: Transforming Defense Strategies with Machine Learning
This research examines the transformative effect of AI, particularly ML, on cybersecurity defense strategies. It delves into how AI transforms threat detection, predictive analysis, automated responses, and ethical frameworks, with integration challenges as a side focus. The qualitative methodology applied, including expert interviews and case studies, indicates that AI improves threat detection accuracy, predictive capabilities, and automated responses significantly, but the challenges of ethics and integration remain central. Such results highlight the urgency for more effective prediction models, adequate contextual responses, and stronger moral guidelines to appropriately utilize AI within cybersecurity.
Zero Trust Network Security: Advancing Cybersecurity in the Remote-First Era
This paper investigates the adoption and efficiency of Zero Trust Architecture in network protection, specifically within a remote-first workplace environment. It analyses the adoption of ZTA within an existing infrastructure as a mechanism for addressing risks of security threats by delving into the concept's principles, difficulties in use, effectiveness for preventing cyber threats, and future prospects. It has relied on qualitative methods where expert interviews and case studies have been conducted for better analysis. The paper discusses findings about ZTA's core principles, challenges faced in remote environments, implications on user experience, and its efficacy in preventing cyber threats. A study claimed that despite ZTA offering the necessary defense mechanism, continuous innovation and adaptation are required to overcome emerging and constantly changing cybersecurity threats.
A Hybrid Deep Learning Framework for Enhanced Anomaly Detection in Industrial Control Systems
Industrial Control Systems (ICS) are increasingly vulnerable to cyberattacks, making robust anomaly detection crucial for maintaining operational integrity and safety. This paper presents a novel hybrid deep learning framework designed to enhance anomaly detection capabilities in ICS environments. The framework combines the strengths of Convolutional Neural Networks (CNNs) for feature extraction from raw sensor data and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for capturing temporal dependencies within system behavior. By integrating these two architectures, the proposed model effectively learns complex patterns and detects subtle deviations indicative of anomalies. The framework is evaluated using a benchmark ICS dataset, demonstrating superior performance compared to traditional machine learning methods and single deep learning models. The results highlight the potential of the hybrid approach for improving the security and reliability of critical infrastructure.
A Hybrid Deep Learning Approach for Enhanced Intrusion Detection in Industrial Control Systems using Feature Selection and Ensemble Techniques
Industrial Control Systems (ICS) are increasingly vulnerable to cyberattacks, posing significant risks to critical infrastructure. Traditional intrusion detection systems (IDS) often struggle to effectively identify sophisticated threats in ICS environments due to the unique characteristics of network traffic and the evolving threat landscape. This paper proposes a novel hybrid deep learning approach for enhanced intrusion detection in ICS, combining feature selection techniques with ensemble learning. The proposed methodology leverages the strengths of multiple deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to capture both spatial and temporal patterns in network traffic data. Feature selection is employed to identify the most relevant features, reducing dimensionality and improving model performance. The ensemble approach combines the predictions of individual deep learning models to enhance accuracy and robustness. The effectiveness of the proposed methodology is evaluated using a publicly available ICS dataset, demonstrating superior performance compared to existing state-of-the-art intrusion detection techniques. The results highlight the potential of the proposed hybrid deep learning approach to significantly improve the security of ICS environments.
Federated Learning with Differential Privacy for Enhanced Security and Personalization in Internet of Things (IoT) Healthcare Applications
The proliferation of Internet of Things (IoT) devices in healthcare has generated vast amounts of sensitive patient data, creating opportunities for personalized and proactive care. However, directly centralizing this data poses significant privacy risks. This paper proposes a novel framework that integrates Federated Learning (FL) and Differential Privacy (DP) to address these challenges. FL enables collaborative model training across decentralized IoT devices without sharing raw data, while DP provides rigorous privacy guarantees by adding controlled noise during the learning process. Our approach enhances security and personalization in IoT healthcare applications by enabling robust model development while preserving patient confidentiality. We present a detailed methodology, experimental results on simulated healthcare datasets, and a thorough discussion of the trade-offs between privacy, accuracy, and communication efficiency. The results demonstrate the feasibility and effectiveness of the proposed framework for improving healthcare outcomes while maintaining stringent data privacy standards. We also explore the challenges of implementing this framework in real-world scenarios and suggest potential future research directions, including adaptive privacy mechanisms and optimized communication protocols
Adaptive Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: A Novel Approach Integrating Convolutional Neural Networks and Recurrent Neural Networks with Attention Mechanisms
The proliferation of Internet of Things (IoT) devices has created a vast and vulnerable attack surface, making intrusion detection a critical component of IoT security. Traditional intrusion detection systems (IDSs) often struggle with the complexity and dynamism of IoT network traffic. This paper proposes a novel Adaptive Hybrid Deep Learning Framework (AHDL-IDF) for enhanced intrusion detection in IoT networks. Our framework integrates Convolutional Neural Networks (CNNs) for effective feature extraction from network traffic data and Recurrent Neural Networks (RNNs) with attention mechanisms to capture temporal dependencies and prioritize relevant features for improved accuracy. The adaptive nature of the framework allows it to dynamically adjust its parameters based on the characteristics of the incoming traffic, enhancing its resilience to evolving attack patterns. We evaluate the performance of the AHDL-IDF using a publicly available IoT network traffic dataset and compare it against existing state-of-the-art IDS models. The experimental results demonstrate that the AHDL-IDF achieves significantly higher detection accuracy, lower false positive rates, and improved adaptability compared to existing approaches, making it a promising solution for securing IoT networks.
Adaptive Hyperparameter Optimization for Deep Learning Models using Reinforcement Learning with Dynamic Exploration-Exploitation Balancing
Deep learning models have achieved state-of-the-art performance in various domains, but their effectiveness heavily relies on the proper tuning of hyperparameters. Traditional hyperparameter optimization methods often suffer from high computational costs and limited adaptability to different datasets and model architectures. This paper proposes a novel adaptive hyperparameter optimization approach that leverages reinforcement learning (RL) with dynamic exploration-exploitation balancing. The RL agent learns to select optimal hyperparameter configurations based on the observed performance of the deep learning model. A key contribution is the dynamic adjustment of the exploration-exploitation trade-off, allowing the agent to efficiently explore the hyperparameter space while also exploiting promising regions. We evaluate our approach on several benchmark datasets and deep learning architectures, demonstrating its superior performance compared to existing hyperparameter optimization techniques in terms of accuracy, convergence speed, and computational efficiency. The results highlight the potential of adaptive RL-based methods for automating and improving the hyperparameter tuning process in deep learning.
Enhancing Wireless Communication Systems through Robust RFI Detection and Classification
Enhancing the Quality of Service (QoS) and security of wireless communication systems necessitates a robust Radio Frequency Interference (RFI) detection mechanism, enabling effective mitigation strategies. This study presents a novel multi-class Multi-Layer Perceptron (MLP) neural network designed for real-time classification of jamming signals in digital video broadcasting based on DVB-S2 standards. The communication signal is assumed to coexist with one of three major interference types: Continuous Wave Interference (CWI), Multiple CWI (MCWI), or Chirp Interference (CI). The proposed algorithm effectively distinguishes interference types and identifies the Signal of Interest (SoI). Principal Component Analysis (PCA) is employed to optimize feature selection, improving classifier performance. Additionally, various learning methods—online, full-batch, and mini-batch—are analysed to determine the most effective approach for real-time applications. A key contribution of this study is the creation of a novel real-time jamming signal dataset, distinct from those used in similar research, allowing for a more comprehensive evaluation of classifier robustness. Performance assessments, conducted at varying Signal-to-Noise Ratios (SNR), demonstrate the classifier's effectiveness in recognizing both known and unknown interference signals. Comparisons with the Support Vector Machine (SVM) technique reveal superior classification and detection capabilities of the proposed MLP-based design. These findings highlight the potential of the MLP approach for real-time RFI detection and mitigation, providing a significant advancement in ensuring the reliability and security of modern wireless communication systems.
Evaluating Machine Intelligence Techniques for Software Effort Estimation with Failure Patterns and Time Constraints
Software effort estimation plays a critical role in project management, especially in the face of failure patterns and time constraints that complicate predictive accuracy. This study evaluates the application of machine intelligence techniques, specifically Hierarchical Temporal Memory (HTM) and Auditory Machine Intelligence (AMI), to improve estimation accuracy. Employing a quantitative methodology, the research investigates five core aspects: comparative effectiveness of HTM and AMI, the impact of failure patterns on estimation accuracy, the influence of time constraints on technique performance, complexity trade-offs, and optimization strategies for real-time applications. Findings reveal HTM's superior accuracy compared to AMI, though its complexity poses challenges for real-time usability. Failure patterns significantly influence estimation accuracy, emphasizing the need for adaptive models. Time constraints were found to affect the performance of both techniques, with HTM displaying greater sensitivity. Optimization techniques demonstrated the potential to mitigate complexity and enhance real-time applicability. This research contributes to the evolving field of software effort estimation by providing a nuanced understanding of the interplay between machine intelligence, failure patterns, and time dynamics. While limitations include reliance on historical datasets and restricted real-time validation, future research can address these gaps by exploring diverse datasets and advanced optimization techniques, offering practical implications for more accurate and efficient software project planning.
Anonymity and Skepticism: The Evolution and Implications of the TOR Network
In today's era of increasing reliance on mobile devices, chatbots play a vital role due to their simplicity and accessibility. The COVID-19 pandemic has further highlighted the insufficiency of healthcare resources, emphasizing the need for scalable digital solutions. This paper presents an application that leverages deep learning to assist with online disease diagnosis via a chatbot interface. The study focuses on predicting an individual’s susceptibility to heart attacks based on specific health indicators. Using a robust dataset, a deep learning model was developed to analyse key features and accurately assess the risk of cardiac events. The model was then integrated into a chatbot, allowing users to access personalized health insights in real-time. By combining advanced machine learning techniques with an intuitive conversational interface, the proposed system aims to enhance early detection and preventive care. The application is designed to reduce the burden on healthcare systems while empowering individuals with critical health information in a user-friendly format. This approach demonstrates the potential of integrating artificial intelligence with conversational platforms to address pressing health challenges effectively.
Role of Chatbots and Deep Learning in Predicting Heart Attack Risk
Analyzing Task Scheduling Algorithms in Cloud Computing for Optimal Performance
Cloud computing has gained significant traction due to its ability to provide scalable, on-demand resources, facilitating efficient data processing and management. A crucial component of cloud performance is task scheduling, which determines how tasks are allocated to remote servers for execution. This paper focuses on analyzing and implementing three widely used task scheduling algorithms: First-Come, First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR). The study aims to evaluate how these algorithms influence cloud system performance, particularly in terms of task execution efficiency, resource utilization, and system throughput. By simulating various cloud environments and workload scenarios, the paper assesses each algorithm's strengths and limitations. The analysis highlights how the selection of scheduling algorithms directly impacts cloud performance, emphasizing the need for optimized task allocation strategies to ensure better system resource management. The findings demonstrate that while each algorithm has its advantages in specific contexts, effective scheduling is crucial for maintaining overall system stability and maximizing resource utilization. The paper concludes with recommendations for selecting the most appropriate algorithm based on workload characteristics and desired performance outcomes, offering valuable insights into improving cloud computing efficiency.