JANOLI International Journal of Artificial Intelligence and its Applications
JANOLI International Journal of Artificial Intelligence and its Applications is a peer-reviewed, open-access publication that publishes articles 12 times a year. that make significant contributions to the field of artificial intelligence, aimed towards scientists, engineers, and business scholars working in the field of artificial intelligence (AI), is a peer-reviewed worldwide publication. Additionally, the Journal plans to publish novel efforts in the form of special issues on developing topics in AI and its applications. Multidisciplinary Subjects: Expert Systems and Intelligent Agents Computer Vision and Image Understanding Natural Language Processing and Conversational AI AI in Healthcare and Diagnostics Autonomous Systems and Robotics AI in Finance and Algorithmic Trading Reinforcement Learning and Game Theory AI for Social Good and Ethical Considerations SCOPE: Cutting-edge Research Core Areas of Artificial Intelligence Applications in Various Domains Interdisciplinary Approach Ethical and Social Implications Innovative Methodologies. AI-driven Solutions Human-AI Interaction. Education and Training Benchmarking and Evaluation International Collaboration
Shanti Educational Research Foundation, India
Detection of Money Laundering Using Graph Neural Networks and Transformer-Based Learning
Money laundering is one of the most significant financial crimes affecting banking institutions, governments, and global financial systems. Criminal organizations use sophisticated transaction strategies to conceal the origins of illegally obtained money and integrate it into legitimate financial systems. Traditional Anti-Money Laundering (AML) systems rely heavily on rule-based monitoring approaches that often fail to detect evolving laundering patterns and generate excessive false positive alerts.This research proposes a hybrid Graph Neural Network (GNN) and Transformer-based deep learning framework for intelligent money laundering detection. The proposed system models financial transactions as graph structures where customer accounts represent nodes and transactions represent edges. Transformer-based attention mechanisms are integrated for sequential transaction analysis and contextual behavior learning. Experimental evaluation demonstrates that the proposed framework achieves superior detection accuracy, higher recall, improved precision, and lower false positive rates compared to conventional machine learning and deep learning techniques. The framework is scalable and suitable for deployment in next-generation AI-driven AML systems.
Expert Approach for Trio Awareness-Based Framework for Automatic Image Captioning Using Visual, Semantic Attention Mechanisms, and HES
Image captioning is the process of analysing the contents of an image and changing them into a coherent Language to explain the image. According to neuroscience research human vision and language formation are connected together. Even if there are many techniques for image captioning, including template filling, and content retrieval, the deep learning-based methods are most popular in current scenario. Feature vectors are generated from an image using an image encoder through the deep learning techniques, and these feature vectors are transformed into a string of words by a language decoder. Various CNNs such as VGG16, VGG19, Inception, and ResNet are available for extracting image features. Various experiments have been performed to find out the best encoder for the purpose of image captioning. LSTM and GRU are two most recently used decoders to generate captions. GRU is the latest version of RNN. It is much faster than LSTM as compared to computational efficiency. Using the encoder-decoder approach or simple attention-based mechanisms has not provided very efficient results. So, visual attention as well as textual attention have been used for efficient image captioning. There are various visual attention mechanisms such as grid-based attention and region-based attention to specifically retrieve image features, and these features are used as additive attention to the decoder. For textual attention, word embedding have been used. In the proposed framework, a trio awareness-based mechanism has been utilized. The objective of this research is to extract Visual characteristics from the region of interest (RoI) of an image and text features using the GloVe embedding technique. The ResNet version of the Convolutional Neural Network (CNN) is utilized as an encoder. A Gated Recurrent Unit (GRU) is utilized as the decoder, and the Human Evaluation Score has been incorporated as a third parameter to further validate the model's performance. The proposed model is tested on the Flickr8k dataset, the findings show that the results attained through the proposed mechanism are more accurate than those of other models
Generative AI in Academic Publishing: A Bibliometric Analysis and Emerging Debates on Integrity, Authorship, and Language Equity
The rapid development of generative artificial intelligence (AI) tools, particularly large language models such as ChatGPT, has generated significant debate within the academic publishing community. This study examines emerging research trends and discussions surrounding the use of generative AI in scientific writing and publishing. Using a bibliometric approach, the study analyses 311 documents indexed in the Scopus database and published between 2023 and 2025. The dataset was processed using the Bibliometrix R package to identify publication trends, frequently used keywords, and thematic relationships. Visualisation techniques, including a word cloud and a keyword co-occurrence network, were employed to map the conceptual structure of the field. The analysis reveals three primary thematic clusters: a technological cluster focused on AI tools and scientific writing, a biomedical-ethical cluster addressing clinical and educational implications, and a publishing-integrity cluster concerned with research ethics, authorship, plagiarism, and peer review. The findings indicate that debates about generative AI in academic publishing are strongly connected to issues of transparency, accountability, and the reliability of scholarly communication. The study highlights the importance of clear editorial policies, responsible AI use, and disclosure practices to maintain research integrity while supporting accessibility and linguistic equity in academic publishing.
Generative Artificial Intelligence, ChatGPT, Academic Publishing, Research Integrity, Bibliometric Analysis, Scientific Writing
Development of a Predictive Model for Heart Dose in Left Breast Radiotherapy Using Geometric Analysis
This paper presents the development of a predictive model to correlate the mean heart dose (MHD) in left breast radiotherapy with treatment geometry, specifically aiming to minimize cardiac complications due to heart proximity. Given the importance of constraining MHD, we propose a methodology to quantify geometric arrangements using the Expansion Intersection Histogram (EIH) method. This approach involves progressive isotropic expansions of the target volume and mapping the overlap with the heart. From the resulting EIH graph, two key parameters—separation (S) and wrapping (W)—are derived, alongside the omolateral breast volume (OBV), which serve as inputs for a multivariate linear regression model. The study includes data from 19 breast cancer patients who received a treatment course of 15 fractions, with a breast dose of 40.5 Gy and a 48 Gy simultaneous integrated boost (SIB) planned via volumetric modulated arc therapy (VMAT). Descriptive statistics for the model parameters showed mean values of 1.21±0.41 cm (S), 8.25±3.33 %/cm (W), and 708.13±388.64 cc (OBV), while the MHD averaged 3.25±0.78 Gy. The regression model demonstrated a high R² value of 0.9, with significant coefficients for S, W, and OBV. The results suggest that the developed model provides a reliable method for predicting MHD based on treatment geometry, offering potential for better heart dose management in radiotherapy.
Copyright (c) 2024 JANOLI International Journal of Artificial Intelligence and its Applications
AI Vendor Performance Assessment in the Mining Industry: A Monte Carlo and LLM Approach
This paper examines the methodologies for assessing AI vendor capabilities in the context of Roy Hill, a prominent iron ore mining company in Western Australia. Through the use of Monte Carlo simulations and Large Language Models (LLMs), including innovative techniques like the Multi-Persona LLM (MP-LLM), we evaluate potential AI vendors to identify those that align with Roy Hill's strategic and operational goals. A robust vendor evaluation framework was developed, integrating survey data with independent assessments of LLMs and their products. The MP-LLM framework was specifically tested for its problem-solving ability and demonstrated enhanced performance when combined with tailored prompt engineering and curated personas. To mitigate challenges such as information asymmetry and confirmation bias, vendor feedback was incorporated, and evaluation metrics were refined using both LLMs and Monte Carlo simulations. The study contributes to AI vendor selection methodologies in the mining sector, emphasizing the importance of adaptive strategies in a rapidly changing technological landscape. Future work will explore advanced techniques such as knowledge graphs and expanded persona libraries to improve AI capability assessments and support operational excellence across industries.
Temporal Dynamics of Vehicle Flow in Interconnected Network Arteries Using Continuous Markov Chains
This study examines the temporal dynamics of vehicle circulation within an interconnected network of arterial roads using continuous Markov chains. Traditional approaches to vehicle flow modeling rely on discrete Markov chains, where each transition represents the passage of vehicles between intersections at fixed time steps. In this paper, we introduce a modification by modeling the process as a continuous system, enhancing the temporal resolution and accuracy of traffic predictions. By representing the network as a digraph and associating it with an ad hoc steady-state matrix, we develop a continuous evolution matrix that allows for the seamless tracking of vehicle populations over time. The model begins with an initial population of vehicles within the network, represented as a vector, and applies the continuous evolution matrix iteratively to predict traffic flow dynamics. This approach improves upon traditional discrete models by enabling finer temporal predictions and providing insights into the steady-state conditions of the system. The results demonstrate the potential of continuous Markov chains to offer more accurate and efficient traffic flow predictions, supporting better traffic management strategies and optimizations for large-scale network arteries.
Optimal Placement and Sizing of Capacitors for Power Loss Reduction in Radial Distribution Systems
This paper introduces a novel and robust approach known as the Slime Mould Algorithm (SMA) for the optimal placement and sizing of capacitors in an IEEE distribution network. Initially, candidate buses for capacitor installation are identified using various indices, including Loss Sensitivity Factors (LSF), Voltage Stability Index (VSI), and Power Loss Index (PLI). These indices aid in determining the most suitable locations for capacitor installation. Subsequently, the SMA is applied to optimize the size and placement of capacitors at these selected buses. The primary objective function focuses on minimizing the total net cost and maximizing annual savings. The proposed method is applied to an IEEE distribution system and its results are compared with other conventional techniques to highlight its effectiveness. The findings demonstrate significant improvements in reducing power losses, lowering costs, and enhancing the voltage profile, thereby showcasing the potential of the SMA for improving the performance of radial distribution networks.
Optimal Capacitor Sizing Voltage Stability Index (VSI)
Speaker Differentiation in AAC Data Logging Using Deep Learning
High-technology augmentative and alternative communication (AAC) devices are essential tools for individuals with complex communication needs. Automated data logging in these devices enables researchers and clinicians to analyze user performance. However, existing systems cannot distinguish between users when multiple individuals access the same device, compromising the validity of data logs and complicating performance evaluation. This paper proposes a deep neural network-based visual analysis approach to address this limitation. By processing video recordings of practice sessions, the method detects and identifies different AAC users, ensuring that data logs accurately reflect individual contributions. This solution has the potential to significantly improve the validity of performance data, streamline analysis, and ultimately enhance AAC outcome measures. Through a combination of advanced video processing and neural network techniques, this approach represents a major step forward in AAC research and clinical practice. It addresses a critical gap in current data logging systems and paves the way for more accurate, user-specific performance evaluation.
Augmentative and Alternative Communication (AAC)
Comparative Study of Machine Learning Techniques for Prediction of Kidney Disease
As kidney chronic disease is nowadays widely increasing which either caused by kidney disease or reduce the function of the kidney, it also affects the cardiac problems- scientifically which can lead to sudden heart attacks at the end-stage. Early diagnosis and adequate therapies can only help in stopping this disease, where dialysis and kidney transplantation is the only way to save the life of the patient. Detecting kidney disease through machine learning and through data mining techniques which can reveal the hidden problem of the kidney. Therefore, the current article is based on the comparative study using various Machine Learning techniques to detect kidney disease. This survey supports to find the accuracy of algorithms which are more useful to find the kidney disease in early stage. The comparative study of all the algorithms and by implementing the models on different platforms, and it is analyzed that which is the best algorithm to predict CKD (Chronic Kidney Disease). The machine learning techniques are compared like Probabilistic Neural Network (PNN), Multilayer Perceptron Algorithm (MLP), Logistic Regression (LOGR), Regression Tree (RPART), Support Vector Machine (SVM) and Radial Basis Function (RBF).
CHATGPT AND DIGITAL COMPETENCES IN THE DEVELOPMENT OF EMPLOYER BRANDING
The article outlines the scope of the issues surrounding the use of ChatGPT as a tool for creating employer branding. It is presented how the artificial intelligence tool can be used in external activities. The aspect related to digital competences, which are very important in the light of ChatGPT, is also taken into account. The research objective of the article is to assess the impact of modern digital tools based on artificial intelligence for employer branding, i.e., employer branding. Equally important is the use of digital competences, which contribute to the application of more advanced digital solutions. In order to address the research problem, a pilot quantitative study was conducted on a random sample of respondents. The research showed that there is low awareness of the existence of modern artificial intelligence tools and, consequently, the strengthening of employer branding activities through the use of innovative solutions. On the other hand, respondents believe that digital competences, i.e., knowledge, skills and experience to be used in the future, are of increasing importance. The research showed links between employer branding, digital competences and ChatGPT. A limitation of the research is the random selection of respondents, as well as the very nature of the pilot study.
Generative AI and Changes in Marketing Policies and Trends
This study focuses on content generation, and personalization, including ethical issues as it examines the effects of generative AI on marketing techniques. Through a thorough analysis of secondary data, researchers pinpoint trends and conclusions. Improved content quality as well as personalized client experiences are made possible by generative AI. However, it is important to address ethical issues like prejudice and privacy. These results highlight the necessity for marketers to take advantage of AI responsibly.
Generative AI and Automation in Business Processes
This study investigates the significant effect of generative AI on the automation of commercial processes. The paper analyses the advantages and disadvantages of generative AI in simplifying processes across several industries, drawing on a wide range of secondary data sources. Enhanced effectiveness, cost savings, increased accuracy, scalability, as well as flexibility have been cited as key strengths. Strong governance structures are required since the incorporation of generative AI presents ethical and privacy issues. Aspects like the effects on the workforce, data security, and industry-specific issues all stand up as important determinants of the adoption of generative AI. The research presented here advances our knowledge of how Generative AI could potentially use to transform company operations.
THE NEW FRONTIER OF CUSTOMER SERVICE IN INDUSTRIES: AN INTRODUCTION TO GENERATIVE AI
In the context of generative AI, this study examines at how customer service is changing across sectors. Researchers determine the benefits and drawbacks of generative AI's influence on productivity, client happiness, and worker dynamics by examining secondary data. The results highlight the necessity of customized strategies including moral concerns in maximizing the potential of generative AI in enhancing customer service. This study adds knowledge that is essential for businesses managing the interface between AI technology and customer service.
A Hybrid Deep Learning Framework for Enhanced Time Series Forecasting in Dynamic Industrial Environments
Accurate time series forecasting is crucial for optimizing operations and decision-making in dynamic industrial environments. This paper proposes a novel hybrid deep learning framework that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) architectures to capture both temporal dependencies and local patterns within time series data. The framework is designed to adapt to the non-stationary nature of industrial processes, incorporating mechanisms for anomaly detection and robust performance in the presence of noise and outliers. We evaluate the performance of the proposed framework on real-world industrial datasets, demonstrating its superior accuracy and robustness compared to traditional time series forecasting methods and individual deep learning models. Furthermore, we analyze the impact of different hyperparameters and architectural configurations on the forecasting performance, providing insights into the optimal design of hybrid deep learning models for industrial time series data. The results highlight the potential of the proposed framework for predictive maintenance, resource optimization, and improved operational efficiency in dynamic industrial settings.
Copyright (c) 2025 JANOLI International Journal of Artificial Intelligence and its Applications
Enhancing Anomaly Detection in Multivariate Time Series Data Using Hybrid Deep Learning Architectures with Attention Mechanisms and Feature Engineering
Multivariate time series anomaly detection is a challenging task that is encountered in most industries like industrial processes, computer security, and health care. Traditional approaches cannot capture within-variable rich temporal relationships as well as cross-stream correlations in high-dimensional streams. This paper presented a new hybrid deep learning model for detecting anomalies in multivariate time series using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) augmented with applications of attention. Within the hybrid model, the LSTM learns specifically temporal dependencies among the multivariate time series, and the CNN learn spatial features embed within the time series. Use of attention mechanism picks out significant features and time steps, which improve improved anomaly detection. Moreover, we proposed a feature engineering technique to extract useful features from raw time series data, which were then inputted into the deep learning model. We empirically demonstrated the proposed method with several benchmark datasets compared to state-of-the-art anomaly detection techniques. The results showed that our proposed model, the integration of attention and feature engineering improved the performance of anomaly detection as a whole (precision, recall, and F1-score).
Hybrid Deep Learning Architecture for Enhanced Intrusion Detection in Industrial Control Systems: A Feature Fusion and Attention Mechanism Approach
Industrial Control Systems (ICS) are becoming highly susceptible to complex cyberattacks, which present major threats to critical infrastructure. Conventional security measures usually fail to counter highly advanced threats (APTs) and zero-day attacks. This paper suggests a novel hybrid deep learning framework for improved intrusion detection within ICS environments. The architecture utilizes feature fusion methods to merge heterogeneous network traffic attributes and uses an attention mechanism to selectively highlight the most informative features towards effective anomaly detection. The model proposed here combines Convolutional Neural Networks (CNNs) for extracting local patterns and Recurrent Neural Networks (RNNs), specifically Gated Recurrent Units (GRUs), for extracting temporal dependencies in network traffic. Experimental results on a benchmark ICS dataset exhibit the better performance of the proposed hybrid model as compared to state-of-the-art intrusion detection systems with improved detection accuracy and reduced false positive rates. The enhanced performance verifies the usefulness of the feature fusion and attention mechanism in promoting the model's capability to recognize subtle and sophisticated attack patterns in ICS networks.
Enhanced Anomaly Detection in Industrial Control Systems using Hybrid Deep Learning Architectures and Federated Learning
Industrial Control Systems (ICS) are becoming more susceptible to cyber attacks, requiring effective anomaly detection mechanisms. In this paper, we introduce a novel anomaly detection framework for ICS networks based on hybrid deep learning models and federated learning. We integrate Long Short-Term Memory (LSTM) networks to analyze temporal sequences with Variational Autoencoders (VAEs) to reconstruct features and detect outliers. In order to meet data privacy requirements and the decentralized environment of ICS deployments, we achieve this using a federated learning paradigm, where model training is conducted across sites without exchanging raw data. The introduced framework is tested on a publicly available ICS dataset and shows better performance than individual deep learning models and conventional anomaly detection methods. The outcomes present enhanced accuracy, precision, and recall in detecting different types of attacks without compromising data privacy. The approach provides a viable and efficient solution towards improving the cybersecurity stance of contemporary ICS ecosystems.
Federated Deep Reinforcement Learning for Personalized Resource Allocation in 5G Network Slicing
5G network slicing offers the potential to tailor network resources to diverse service requirements, but efficient and personalized resource allocation remains a significant challenge. Traditional centralized approaches struggle with scalability, privacy concerns, and the dynamic nature of user demands. This paper proposes a novel Federated Deep Reinforcement Learning (FDRL) framework for personalized resource allocation in 5G network slicing. The framework leverages federated learning to train a global deep reinforcement learning agent collaboratively across multiple edge servers, without sharing raw user data. Each edge server acts as a local agent, learning optimal resource allocation policies based on its local user data and contributing to the global model update. The proposed FDRL framework is designed to address the limitations of centralized approaches by enabling personalized resource allocation while preserving user privacy and enhancing scalability. We evaluate the performance of the FDRL framework through extensive simulations, demonstrating its superiority over centralized and non-federated DRL approaches in terms of resource utilization, service satisfaction, and privacy preservation. Furthermore, we analyze the impact of key parameters, such as the number of federated clients and the degree of data heterogeneity, on the performance of the FDRL framework.
Integrating Machine Learning and Soft Computing for Smarter Problem-Solving Solutions
This research deals with the integration of machine learning (ML) and soft computing techniques to make problem-solving across all domains much better. The paper investigates the predictive accuracy, adaptability and efficiency of decisions, as influenced by synergy between these methodologies. Reviewing existing literature and conducting case studies, the paper tries to identify benefits, challenges, and applications of this integration. The findings reveal that ML and soft computing together provide improved flexibility, overcome technical barriers, and enable the development of hybrid models with applications in fields such as healthcare, finance, and energy. Further research is still needed to refine frameworks and expand the application of integrated solutions in new domains.
Innovations and Challenges in the Future of Deep Learning: A Comprehensive Exploration of Artificial Intelligence
This paper reviews and analyzes the developments of deep learning in AI research, focusing particularly on innovations along with emerging problems. It points out how emerging developments and potential issues affect development and deployment through deep learning innovations. The analysis looks into innovation in new structures, data poor and data deficiency, AI interpretation, ethical factors, and both social and economic effects of DL. A qualitative methodology involving literature review and expert interviews is applied in order to insight these areas. The paper reveals some critical advancements in architectures developed for neural networks, improvement in data quality, interpretability, ethical considerations, and societal impact. Yet, the research casts aside other present major open problems of scalability, data bias, lack of transparency, and ethical dilemmas that significantly shape the future trajectory of deep learning.
Integrating Soft Computing Techniques: A Comprehensive Exploration of Fuzzy Logic, Neural Networks, and Evolutionary Algorithms
This paper discusses how soft computing techniques, such as fuzzy logic, neural networks, and evolutionary algorithms, can be integrated to advance computational problem-solving capabilities. This study synthesizes existing literature and case studies in order to illustrate the strengths and limitations of such integration, including the potential benefits and challenges. It studies the theoretical relevance and practical applicability of this integration across different domains such as robotics, bioinformatics, finance, and environmental modeling. The research methodology would involve qualitative analysis in the form of literature review, expert interviews, and thematic analysis of case studies, to understand the overall development prospects of the future for integrated soft computing techniques. The findings suggest that while these methods, when integrated, bring in considerable improvements in problem-solving and real-world applications, scalability and resource allocation issues still prevail, thus necessitating further research and innovation for optimal utilization.
Deep Learning Applications: Transformative Impacts in Vision, Language, and Emerging Fields
This paper explores the transformative role of deep learning in computer vision, natural language processing, and cross-disciplinary applications, analyzing the profound impact that deep learning is having across domains. It further explores advancements in image recognition, the evolution of NLP with models like BERT and GPT, innovations across domains, ethical implications of AI technologies, and future trends in the field. The research study uses a qualitative approach and consolidates findings based on case studies, literature reviews, and interviews with experts. Key results: transformer-based models are superior compared to other variants in image and language tasks, ethical frameworks matter, and pursuit of sustainable advances. The last recommendation is interdisciplinary cooperation and responsible deployment of AI.
Intelligent Algorithms: Transforming Industries through Advancements in Machine Learning
This paper discusses the transformative impact of the development of new machine learning algorithms across the industry spectrum on data analysis, automation, customized customer experience, integration issues, and workforce implications. The research, conducted with a qualitative approach and based on industry case studies and interviews with experts, probes into how intelligent algorithms are rewriting practices and results. Major areas of focus would include real-time data processing and predictive analytics; personalized services on the one hand, and change resistance and limitations in infrastructure to integrate these emerging technologies on the other. Based on this paper, reskilling the workforce and balancing with ethical considerations around user privacy could be some crucial takeaways of this research to understand the transformational potential that machine learning is capable of for industries.
Predictive Modeling of Cardiac Arrhythmias Using Hybrid Feature Selection and Ensemble Learning Techniques
Cardiac arrhythmias pose a significant threat to global health, necessitating accurate and timely diagnosis for effective treatment. This research investigates the application of hybrid feature selection techniques combined with ensemble learning methods to improve the predictive accuracy of cardiac arrhythmia classification. We propose a novel approach that integrates filter-based (Information Gain) and wrapper-based (Genetic Algorithm) feature selection to identify the most relevant electrocardiogram (ECG) features. These selected features are then utilized to train various ensemble models, including Random Forest, Gradient Boosting Machines (GBM), and XGBoost. The performance of these models is evaluated using a comprehensive dataset of ECG recordings, and the results demonstrate a significant improvement in classification accuracy, precision, recall, and F1-score compared to traditional machine learning approaches. The proposed methodology offers a robust and efficient solution for cardiac arrhythmia prediction, potentially aiding clinicians in early diagnosis and personalized treatment planning.
Context-Aware Attentive Deep Learning for Enhanced Sentiment Analysis in Multimodal Social Media Data
Sentiment analysis, the task of recognizing and classifying opinions contained in text, has undergone substantial growth with the application of deep learning. Nevertheless, its performance is usually compromised by its dependence on text data only, while ignoring the wealth of information captured in other modalities such as images and videos that are ubiquitous in social media. Furthermore, current methods usually lack the ability to capture properly the contextual subtleties contained in multimodal data. This work presents a new Context-Aware Attentive Deep Learning (CAADL) framework for improved sentiment analysis in multimodal social media. CAADL uses deep learning models with attention mechanisms to capture informative features from both text and visual modalities. Additionally, it makes use of contextual information using a hierarchical attention network that captures inter-modal and intra-modal interactions. The proposed framework is trained and tested on a large-scale multimodal sentiment analysis dataset. Experimental outcomes verify that CAADL strongly surpasses state-of-the-art baselines with regard to precision, F1-score, and accuracy, proving the significance of attention mechanisms and context awareness in multimodal sentiment analysis. The introduced framework offers an effective and strong solution for interpreting and understanding sentiments in the intricate and dynamic world of social media.
Hybrid Attention-Guided Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks
The mushrooming of Internet of Things (IoT) devices has provided an enormous attack surface, and thus IoT networks have become very susceptible to numerous cyber threats. Classical intrusion detection systems (IDS) tend to be ineffective in detecting sophisticated and changing attack patterns in such dynamic networks. This paper outlines a novel hybrid attention-guided deep learning paradigm for advanced intrusion detection in IoT networks. The system combines convolutional neural networks (CNNs) to extract features, recurrent neural networks (RNNs) with an attention mechanism to model temporal dependency, and a deep neural network (DNN) for prediction. The attention mechanism enables the model to pay attention to the most important features in the detection process, enhancing accuracy and minimizing false positives. The effectiveness of the presented framework is analyzed based on the NSL-KDD dataset, which proves its high performance compared to other state-of-the-art IDS methods based on detection accuracy, precision, recall, and F1-score. The outcomes show the effectiveness of the hybrid attention-guided deep learning model in securing IoT networks from intelligent cyberattacks.
A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Integrating Federated Learning and Attention Mechanisms
The exponential growth in Internet of Things (IoT) devices has opened up an enormous and exposed attack surface, necessitating stronger intrusion detection systems (IDS). Conventional centralized IDS solutions are not scalable and do not ensure privacy for the distributed IoT paradigm. This paper introduces a new hybrid deep learning model combining federated learning and attention mechanisms to provide improved intrusion detection for IoT networks. The framework capitalizes on the decentralized aspect of federated learning to learn a global model cooperatively across IoT devices without exposing sensitive information. Attention mechanisms are built into the deep learning framework to attend to the most informative features for effective anomaly detection. We apply and test the designed framework on a benchmark IoT intrusion detection dataset, achieving substantial improvement in detection accuracy, minimizing communication overhead, and privacy enhancement over current state-of-the-art techniques. The results highlight the promise of this hybrid solution for designing robust and privacy-compliant security solutions for the fast-growing IoT domain.
Enhancing Spatio-Temporal Traffic Prediction through Hybrid Deep Learning Architectures and Attention Mechanisms
Precise traffic forecasting is an important element of Intelligent Transportation Systems (ITS), facilitating proactive traffic control, congestion relief, and enhanced mobility. The conventional statistical and machine learning approaches tend to struggle in capturing the intricate spatio-temporal interdependencies in traffic stream, restricting their utility in dynamic and congested environments. Recent developments in deep learning—e.g., Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs)—are proving useful, but a single architecture is unable to capture the multi-faceted nature of traffic data. This paper presents a new hybrid deep learning model combining CNNs for spatial feature extraction, RNNs for temporal sequence modeling, and GNNs for modeling road network topologies, supported by attention mechanisms to weight dynamic relevant features. The model is tested on real-world highway traffic flow data and compared with ARIMA, LSTM, and Spatio-Temporal Graph Convolutional Networks (STGCN). Results show that the solution presented in this work attains better performance on various metrics (MAE, MSE, RMSE, MAPE) compared to baseline models and shows resilience under different traffic conditions. The results confirm the potency of hybrid models and attention mechanisms in improving traffic prediction and paving the way toward more accurate and efficient ITS technologies.
Synergistic Integration of Graph Neural Networks and Reinforcement Learning for Enhanced Dynamic Resource Allocation in Cloud Computing Environments
Cloud computing provides scalable and cost-effective infrastructure, but dynamic resource allocation remains a major challenge due to unpredictable workloads, heterogeneous resources, and diverse application requirements. Traditional static or rule-based methods lack adaptability, while machine learning approaches often fail to capture complex dependencies. Reinforcement Learning (RL) offers adaptability but struggles with scalability in large state spaces, and existing graph-based methods rely heavily on manually defined relationships.In order to overcome these constraints, we present in this paper an integrated scheme that combines RL and Graph Neural Networks (GNNs) to solve the problem of dynamic resource allocation. The cloud infrastructure is graphically modeled where the virtual machines, servers, and network devices are treated as nodes and their dependencies as edges. The GNN captures the deep features of such relationships, which are subsequently utilized by an RL agent to make decisions for allocation. We analyze the suggested GNN-RL framework through CloudSim, contrasting it with static, threshold-based, and isolated RL approaches. The results show considerable improvements in the utilization of resources, the completion time of tasks, and power consumption. Our study indicates the value of integrating GNN-RL towards providing a scalable and flexible solution for managing resources in cloud environments towards enabling more intelligent and efficient cloud computing systems
Context-Aware Federated Learning for Enhanced Predictive Maintenance in Industrial IoT
This paper investigates the application of context-aware federated learning (CAFL) to enhance predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments. The inherent challenges of IIoT, including data heterogeneity, privacy concerns, and resource constraints, limit the effectiveness of traditional centralized machine learning approaches for PdM. CAFL addresses these challenges by enabling collaborative model training across distributed edge devices without directly sharing raw data. We propose a novel CAFL framework that incorporates contextual information, such as operating conditions and environmental factors, to improve the accuracy and robustness of PdM models. The framework is evaluated using a simulated IIoT environment with diverse machine types and operating conditions. Experimental results demonstrate that CAFL significantly outperforms traditional federated learning and centralized learning approaches in terms of prediction accuracy, model generalizability, and data privacy preservation. The paper concludes by discussing the implications of CAFL for future IIoT applications and outlining potential avenues for further research.