JANOLI International Journal of Applied Engineering and Management
JANOLI International Journal of Applied Engineering and Management Sciences is an open-access journal affiliated with Shanti Educational Research Foundation India The journal focuses on disseminating the latest research in the field of applications of engineering in Management Science with all its related domains and its role in decision-making, as well as their relationships to cognate disciplines including all in engineering and management. The journal adheres to a stringent double-blind peer review policy to maintain the good quality of research publication. With an acceptance rate of 15% of submitted manuscripts, the journal encourages recent discoveries and innovations comprising theoretical, conceptual and empirical research in management and applied research. The Journal also publishes extensively for managerial IT professionals and industry practitioners in the domain of economics, business management, information systems and decision analysis. Multidisciplinary subjects: Industrial Automation and Smart Manufacturing Project and Process Management Sustainable Engineering and Green Technologies Quality Control and Lean Six Sigma Engineering Economics and Cost Analysis Technology Management and Innovation Safety Engineering and Risk Assessment Operations Research and Systems Optimization
Shanti Educational Research Foundation, India
Exploring Live Streaming as a Game-Changer for Direct - to - Consumer Marketing During COVID-19
The COVID-19 pandemic forced the closure of physical retail stores, compelling brands to explore innovative ways to engage with customers. Live Streaming emerged as a powerful tool, enabling brands to maintain customer access and foster engagement even during lockdowns. This study explores the potential of livestreaming as a viable medium for direct-to-consumer communication and interaction. This paper presents a conceptual framework for using live streaming in marketing, developed through focus group discussions and model analysis based on the ABCD listing methodology. The study examines the dynamics of livestreaming within retail and ecommerce, focusing on its ability to engage both external customers and internal stakeholders. The research reveals that engagement should extend beyond customers to include business partners and internal employees, demonstrating that live streaming is not only a tool for retail sales but also a platform for broader organizational engagement. The findings suggest that livestreaming is a long-term asset for the retail and e-commerce industries, even beyond the pandemic’s impact. The study acknowledges the widespread business disruptions caused by the pandemic, which led to decreased demand and hindered operations globally. The limitations also highlight the gaps in business interruption insurance policies, emphasizing the need for businesses to adapt and reconsider existing policies. The pandemic's economic damage remains largely unaddressed. This paper highlights the latest trends in live streaming, offering valuable insights into its application as an integrated marketing communication channel. It underscores the importance of adapting to new technologies and approaches for sustained customer engagement and business resilience.
Copyright (c) 2024 JANOLI International Journal of Applied Engineering and Management
Towards Transparency in AI: A Review of Explainable AI (XAI) Approaches and Research Opportunities
As Artificial Intelligence (AI) continues to infiltrate various sectors, from healthcare to finance, the ability to trust AI-driven decisions becomes crucial. Machine learning (ML) models, though highly accurate, often operate as "black boxes," making it difficult to understand their decision-making processes. This lack of transparency creates significant challenges in critical areas like medical diagnosis and financial transactions, where understanding the reasoning behind decisions is vital. In particular, ensemble models like Random Forests and Deep Learning algorithms, while improving prediction accuracy, exacerbate the issue of interpretability. This paper reviews the current challenges in explaining ML predictions and explores existing approaches to Explainable Artificial Intelligence (XAI). Through an extensive literature review of research from reputable sources, we identify key gaps in current methods and provide insights into opportunities for future development. While some algorithms, such as Decision Trees and KNN, offer built-in interpretability, there is no universal solution for explaining the outcomes of complex models. The paper proposes a conceptual framework for developing a common approach to XAI that can address these challenges, providing clarity and consistency in decision explanations. Finally, the paper outlines future research directions to improve the interpretability and adoption of AI models in various sectors.
Explainable Artificial Intelligence (XAI)
Sector-Wise Investment Return Dispersion in Indian CPSEs: A Comprehensive Study of Aggregation Methods
Central Public Sector Enterprises (CPSEs) are integral to India’s economic framework, providing critical goods and services. To ensure long-term sustainability, CPSEs must consistently generate reasonable profits. A higher dispersion in investment returns indicates greater risk. Therefore, this study analyses the industry-wise dispersion of investment returns in Indian CPSEs over the period from 2010-11 to 2019-20. Secondary data was utilized for the study. Dispersion in investment returns was assessed using the Coefficient of Variation (CV). Additionally, a paired t-test was employed to identify significant changes in average dispersion across the selected period, and one-way ANOVA was used to examine differences in investment returns among various industries. The findings show significant variability in the rate of investment returns across industries, with some industries displaying better consistency in returns during the first sub-period compared to the second. The study also revealed significant differences in investment returns across industries, underscoring the impact of each industry’s performance on the overall investment returns of CPSEs. This study provides valuable insights into the dispersion of investment returns in CPSEs, offering a clearer understanding of the inherent risks across different industries and their cumulative effect on the financial health of CPSEs.
A Virtual 3D Printer Model for Robotic System Design in CoppeliaSim
3D printers are gaining significant popularity in diverse fields, especially for prototyping and product development. However, physical 3D printers are expensive, bulky, and challenging to transport, which poses difficulties for researchers who need access to these devices for testing algorithms and prototypes. This research proposes an alternative solution by leveraging the CoppeliaSim simulator to create a virtual 3D printer model. Using the popular and affordable Ender 3D printer specifications, we build a detailed simulation of the printer within CoppeliaSim, a powerful open-source robotics simulation environment. The process is simplified through a step-by-step guide, allowing researchers to quickly create the model, control it with LUA scripting, and simulate printing tasks such as drawing a square on the print bed. All relevant project files and code are made available on GitHub, enabling researchers to easily download and integrate the model into their own work. The simulation provides an accessible platform to test 3D printing algorithms, analyse 3D print files, and explore printer functionality without the physical constraints of a real printer. Researchers can further adapt the model by modifying physical parameters or designing custom 3D printers. This method offers a cost-effective, flexible, and practical solution for 3D printing research, allowing faster iteration and experimentation, particularly for those with limited resources or space.
Innovative ICT Approaches to Empower Smart City Development
Smart Cities are undergoing rapid transformation driven by advanced tools, technologies, and significant investments worldwide. These cities aim to balance growth with environmental sustainability, emphasizing reduced carbon footprints, lower harmful emissions, and optimized energy consumption. Despite progress, Smart Cities face critical challenges, including traffic congestion, water scarcity, energy inefficiencies, waste management, limited citizen participation, and complex IT infrastructure maintenance. These challenges, although varying in scope and scale, are common across regions.This paper examines key challenges encountered during the development, operation, and maintenance of Smart Cities and explores how ICT and digital technologies address these issues effectively. High-impact technologies such as IoT, AI, Machine Learning, Blockchain, Data Analytics, Digital Twin, 5G, and Cloud Computing play a pivotal role in delivering innovative solutions.Using a systematic literature review and primary data from our research, the study highlights the application of these technologies in resolving operational and developmental challenges. Case studies and insights from industry professionals underscore the transformative potential of digital tools while acknowledging certain limitations.The findings emphasize the importance of prioritizing solutions for pressing issues in Smart Cities and offer recommendations for enhancing the deployment of these technologies. Future plans are discussed to achieve full-scale implementation and address current limitations, fostering continuous innovation and improvement in Smart City services.
An Introduction to Amorphous Flat Movies' Visual Characteristics
Substrates of glasses have been coated with thin sheets of the shiny metals using a vacuum- assisted heat-evaporation process. The films' transmission signals were measured between 400 and 1200 nm in frequency. Swanepoel's simple approach is used to estimate optical constants such as reflection index, genuine dielectric force, and extinction coefficient. Here, we are introducing some facts and insights about such type of substances.
THE IMPACT OF REMOTE WORK ON EMPLOYEE ENGAGEMENT AND PRODUCTIVITY
This paper provides a thorough examination of peer-reviewed literature examining the effects of remote work on productivity and employee engagement. It gets important insights into the intricacies of remote work by analysing four publications, including one longitudinal research, a work efficiency comparison, a review of the literature on employee satisfaction, and a qualitative investigation of communication difficulties in virtual teams. Although a productivity comparison offers unbiased perceptions into the real-world work hours as well as tasks of remote compared in-office employees, the longitudinal analysis illustrates the shifting dynamics of staff participation in work-from-home arrangements over time. The analysis of the examined papers revealed a number of strengths and drawbacks. Longitudinal designs, sizable sample sizes, measurable productivity indicators, and thorough meta-analysis methodologies are among its advantages. The results show how crucial it is to take into account both productivity and worker engagement when assessing the effects of remote work. In order to improve communication and cooperation in virtual teams and increase satisfaction with work, organisations should implement evidence-based solutions.
Interaction Monitoring: A Secure Covid-19 Charting Framework Using the Cloud
Due to touch spread, the latest coronavirus outbreak (COVID-19) was spreading daily among people and was brought on by the virus called SARS-CoV-2. Managing epidemics requires the identification of infectious disorders like COVID-19 and the prevention of their spread. Finding or investigating other transferable people was a key step in determining and tracking their past relationships, which will help in the cautious dissociation of any individuals who may have been contaminated and enable spread. Smart devices, like smartwatches and smartphones, can be used to track down these prior interactions. By the use of digital watches and mobile device monitoring techniques, and developed a COVID-19 epidemic simulation to assess the quantity of people isolated and the efficacy of the action that needs to be taken. Our research paper's findings demonstrate that, to be precise and successful in combating the COVID-19 pandemic, tracking contact methods need to be used quickly, a significant portion of the general public has to utilize smart devices, and the gadgets used for connection monitoring need to be efficient. Because of all these strict requirements, contact tracking via smart devices was not very effective in containing the illness throughout the COVID-19 pandemic. But during this stage, intelligent machine-based tracking of connections might prove to be very helpful in identifying another stage in which a subset of the population will be more immune.
Impact of Coronavirus on Wearable Tracking Devices Based in the Global Cloud
In 2020, a virus starts making news throughout the world because of its extraordinary ability to spread from person to person. The corona virus is this virus. The corona virus illness of 2019 (COVID-19) has killed thousands of people in several nations, including Italy, the United States, India, Brazil, China, and others. Not a single person is spared from this epidemic; COVID-19 affects millions of individuals worldwide. However, this has an impact on a number of factors, one of which is wearable technology, such as smart watches, smart bracelets, smart shoes, smart belts, etc. Wearable technology is becoming more and more important in this age of digitization, especially in light of the current COVID-19 epidemic. Therefore, we will talk about the COVID-19 pandemic's part in the market's rise as well as its effects in this essay. This essay presents wearable technology's uses and gives a quick explanation of how it may be used to combat the current epidemic. Remote health monitoring, screening, real-time tracking, improved treatment without spreading the illness to other people, and surveillance were all approved by these gadgets. As a result, it lessens the workload for the healthcare sector in terms of corona virus prevention and transmission reduction. Millions of people worldwide suffer from respiratory conditions that call for constant observation in order to be treated. This wearable gadget assists medical professionals in monitoring the breathing parameters of patients infected with the corona virus and offers ways to obtain the patient's critical information in the event of a chronic sickness. The other people's perspective is impacted by hearing about patients' progress, which raises tension and anxiety levels. Wearable technology that monitors a person's heart rate variability can help them reduce their stress and anxiety (HRV).
The Greater Risk to Startups in the Buyer Arena is Security
The study article that follows expands on our knowledge of a start-up's confidentiality issues from the viewpoints of two parties. The first viewpoint reflects that of a start-up, which means that the business should maintain the confidentiality of the personal details of its consumers. The other point of view is that of a businessperson, which means that the concept of the start-up should remain private. Thus, protecting your concept and information for a new company becomes a demanding task. To reach the only goal of starting a start-up, one of the most important steps that must be given thoughtful consideration is safeguarding and preserving one's concept and data relevant to their startup company. We will also comprehend the significance of confidentiality in the consumer environment and the potential repercussions should such security be violated or abused by businesses. We will also learn how these businessmen and start-up businesses defend their own against these kinds of security lapses and data stealing. This paper will undoubtedly go into great detail on all the issues and probable remedies pertaining to the security of an entrepreneur's and client's data.
The Algorithmic Augmentation of Customer Lifetime Value Prediction: A Comparative Analysis of Machine Learning Models in the Retail Sector
This study examines the effectiveness of machine learning models in predicting Customer Lifetime Value (CLV) in the dynamic retail environment. Precise CLV prediction facilitates targeted marketing, resource optimization, and improved customer relationship management. We evaluate the performance of various machine learning models, such as Linear Regression, Support Vector Regression (SVR), Random Forest Regression, and Gradient Boosting Regression, on a rich dataset of customer transactions and demographic data from a big retail chain. The research employs feature engineering methods to enhance model performance and mitigates possible biases in the data and algorithms. In addition, we examine the effect of different evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, on model choice. The results offer useful insights for retail practitioners who want to apply machine learning for CLV prediction and guide future research in this field. This research adds to the expanding literature on algorithmic marketing and highlights the need for responsible and ethical use of predictive models in business.
Copyright (c) 2025 JANOLI International Journal of Applied Engineering and Management
The Algorithmic Alchemist: Unveiling the Synergistic Potential of Machine Learning and Behavioral Finance in Predicting Market Sentiment and Optimizing Investment Strategies
This research investigates the synergistic potential of integrating machine learning (ML) techniques with behavioral finance principles to enhance market sentiment prediction and optimize investment strategies. Traditional financial models often fail to account for the irrationalities and cognitive biases that significantly influence market behavior. This study leverages advanced ML algorithms, including recurrent neural networks (RNNs) and sentiment analysis tools, to extract and interpret market sentiment from diverse data sources, such as news articles, social media, and financial reports. By incorporating behavioral biases, such as loss aversion and herding behavior, into the ML models, we aim to develop more accurate and robust predictive models. Furthermore, we propose an algorithmic trading framework that utilizes the predicted market sentiment to dynamically adjust investment portfolios, minimizing risk and maximizing returns. The results demonstrate the effectiveness of the proposed approach in outperforming traditional investment strategies, highlighting the transformative potential of combining ML and behavioral finance in navigating the complexities of modern financial markets.
The Algorithmic Augmentation of Customer Lifetime Value Prediction: A Hybrid Approach Integrating Machine Learning and Traditional RFM Analysis
Predicting Customer Lifetime Value (CLTV) is crucial for effective marketing resource allocation and strategic customer relationship management. This paper proposes a novel hybrid approach that integrates traditional Recency, Frequency, and Monetary (RFM) analysis with advanced machine learning techniques to enhance the accuracy and robustness of CLTV predictions. We develop and evaluate several machine learning models, including regression algorithms and classification models for churn prediction, and compare their performance against traditional RFM-based methods. The proposed hybrid model leverages the strengths of both approaches, using RFM scores as features within the machine learning models. Empirical results, derived from a real-world transactional dataset, demonstrate that the hybrid model significantly outperforms both traditional RFM analysis and individual machine learning models in predicting CLTV, leading to improved marketing ROI and customer retention strategies. Furthermore, the paper provides insights into the key factors driving customer lifetime value and offers practical recommendations for businesses to optimize their customer engagement strategies.
The Algorithmic Augmentation of Customer Lifetime Value Prediction: A Hybrid Approach Integrating Machine Learning and Traditional Marketing Metrics
Customer Lifetime Value (CLV) prediction is crucial for effective marketing resource allocation and strategic decision-making. Traditional CLV models often rely on simplifying assumptions and aggregated historical data, limiting their predictive accuracy. This research proposes a hybrid approach that integrates machine learning algorithms with traditional marketing metrics to enhance CLV prediction. We develop a model incorporating both transactional data and customer behavioral features extracted from CRM systems. The methodology involves feature engineering, model selection (comparing algorithms such as linear regression, decision trees, random forests, and gradient boosting), and rigorous model evaluation using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. Our results demonstrate that the hybrid model, particularly the gradient boosting algorithm, significantly outperforms traditional CLV models in predicting future customer value. The findings offer actionable insights for marketers to optimize customer acquisition, retention, and engagement strategies. The paper concludes with a discussion of limitations and potential avenues for future research, including the incorporation of real-time data and advanced deep learning techniques.
Algorithmic Trust and Financial Decision-Making: Examining the Moderating Role of Financial Literacy and Risk Aversion
This paper investigates the impact of algorithmic trust on financial decision-making, specifically focusing on investment choices. We examine the moderating roles of financial literacy and risk aversion in this relationship. Using a mixed-methods approach combining quantitative surveys and qualitative interviews, we analyze how individuals' trust in algorithms influences their investment decisions, considering their levels of financial literacy and risk aversion. Our findings reveal a complex interplay between these factors. While higher algorithmic trust generally correlates with increased adoption of algorithm-driven financial advice, this effect is significantly moderated by financial literacy. Individuals with high financial literacy exhibit a more nuanced approach, calibrating their trust based on the perceived transparency and explainability of the algorithm. Conversely, those with lower financial literacy tend to rely more heavily on algorithmic cues, potentially leading to suboptimal financial outcomes. Risk aversion further complicates the relationship, influencing the type of investment individuals are willing to make based on algorithmic recommendations. This research contributes to the growing body of literature on behavioral finance and fintech, providing insights for policymakers, financial institutions, and algorithm developers seeking to promote responsible and effective use of AI in financial services.
Strategic Marketing Investments: Driving Financial Growth and Business Success
This paper explores the relationship between marketing investments and financial development, stressing their impact on organizational success. The study delves into how strategic investments in marketing influence financial growth by answering five key subquestions: identifying high-ROI marketing strategies, the influence of marketing on consumer perceptions, the role of digital marketing in financial growth, the sustainability of marketing practices, and managing risks in marketing investments. A qualitative approach, including interviews with experts and case studies, gives insights into the dynamics between marketing and financial performance. The findings highlight the importance of integrated marketing strategies, the transformative impact of digital innovation, and the role of sustainability in achieving sustainable growth. Moreover, effective risk management is revealed as a critical tool in maximizing returns and ensuring competitiveness. This paper provides valuable insight but calls for future research into the use of quantitative methods and a broader scope of sectors in order to really understand the financial implications of marketing.
Evaluating Brand Equity: Integrating Marketing Strategies with Financial Metrics
This study will discuss integration of marketing strategies and financial metrics for assessing brand equity. Based on marketing investments, brand loyalty, awareness, association, and customer perceptions, the paper explores which factors would affect financial performance. A regressions analysis was used in this study with data from 2010 to 2023 to validate the hypotheses in a quantitative approach. The results of the study are as follows: strategic marketing investments enhance brand equity, brand loyalty drives profitability, brand awareness boosts market value, brand associations strengthen competitive advantage, and positive customer perceptions result in better financial outcomes. The longitudinal impacts and comprehensive frameworks in the literature were missing; therefore, this research provides evidence that marketing strategies must be aligned with financial metrics to succeed in building brands.
Examining the Influence of Financial Analytics on Data-Driven Marketing Campaigns
This study explores the nexus of financial analytics and marketing by analyzing how data-driven strategies enhance marketing campaign design and effectiveness. Using quantitative methods, the study identifies financial analytics as an independent variable and marketing performance metrics as dependent variables. It investigates five key areas: campaign targeting accuracy, budget optimization, customer segmentation, ROI measurement, and strategic integration of financial insights. Validating hypotheses that advanced financial analytics significantly enhance targeting, budget allocation, precision in segmentation, accuracy in ROI measurement, and strategic effectiveness, data from campaigns between 2015 and 2023 resonates with the results above. The role of financial analytics, though, remains central to the argument about ensuring optimality of marketing outcome, yet limitations include reliance on historical data and challenges associated with cross-industry data integration. However, future studies should focus on emerging technologies and various conditions of markets to deepen insights on these flows.
Financial Strategies for Optimal Budget Allocation in Digital Marketing
This paper finds the financial dynamics behind budget allocation in digital marketing, emphasizing its role in driving marketing success and fostering organizational growth. The study largely focuses on the impact of allocation strategies on ROI, market adaptability, channel selection, performance-based budgeting, and brand engagement. A quantitative methodology is used to analyze data from 2015 to 2023 to validate five hypotheses on these areas. Findings indicate a direct relationship between strategic budgeting and sustained ROI growth, flexible budgets enhance adaptability, channel selection impacts financial results, performance-based budgeting helps optimize campaigns, and effective strategies improve brand engagement. The conclusion of the paper provides recommendations for applying budgeting strategies to enhance marketing outcomes while bridging gaps in data and needs for further research.
Strategic Synergies between Marketing and Finance for Maximizing Return on Investment
This study explores the critical interface between marketing and finance, focusing on the integration of financial insights into marketing strategies to optimize return on investment (ROI). The paper addresses five sub-research questions: the impact of financial analytics on marketing decision-making, strategic budget allocation on financial performance, financial metrics on campaign success, marketing-finance collaboration on resource optimization, and integrated financial strategies on customer acquisition and retention. A quantitative method is used with data from varied industries for 2015 to 2023 to determine associations between financial tools and marketing performance. Findings support hypotheses regarding the positive effect of financial analytics, strategic budgeting, and cross-functional collaboration on marketing effectiveness, resource utilization, and customer retention. The study concludes by emphasizing the synergy between marketing and finance as critical to sustainable business growth, and by suggesting avenues for future research into diverse financial instruments and contexts.
Enhancing Predictive Maintenance in Industrial Machinery Using Hybrid Deep Learning Models with Sensor Fusion and Anomaly Detection
This study examines the use of hybrid deep learning architectures for improving predictive maintenance practices in industrial equipment. The method incorporates sensor fusion methodologies to take advantage of information from various sensor modalities (vibration, temperature, pressure) and utilizes anomaly detection methods to detect abnormal performance from normal working conditions. A hybrid architecture, which uses Convolutional Neural Networks (CNNs) for features extraction and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies, is suggested. The performance of the model is verified on an actual dataset of industrial pump operation, exhibiting excellent gains in prediction accuracy and minimized false alarm rates over conventional methods. The findings indicate the proposed approach's potential for proactive maintenance scheduling, downtime reduction, and operational efficiency optimization.
Predictive Maintenance Optimization for Industrial Machinery using Hybrid Deep Learning and Vibration Analysis
This study explores predictive maintenance optimization for industrial equipment through the use of a hybrid methodology combining deep learning methods with conventional vibration analysis. The research responds to the crucial requirement to reduce downtime and maintenance expenditures in industry by creating a prediction model that clearly predicts impending equipment failures. We introduce a hybrid model that is comprised of Convolutional Neural Networks (CNNs) for extraction of features from raw vibration signals and Long Short-Term Memory (LSTM) networks for analyzing time-series data and predicting failures. The model is trained and validated with a thorough dataset of vibration signals gathered from different industrial equipment under different operating conditions. The findings indicate that the suggested hybrid strategy performs better than conventional methods according to prediction accuracy, lead time, and overall maintenance cost savings. The research shows the capability of vibration analysis with deep learning for proactive scheduling of maintenance and enhanced operation efficiency in industrial settings.
Enhanced Predictive Maintenance for Industrial Machinery using Hybrid Machine Learning and IoT Sensor Fusion
This paper proposes a upgraded predictive maintenance (PdM) framework for industrial equipment based on a hybrid machine learning strategy combined with IoT sensor fusion. The proposed framework combines information from diverse sensor modalities (acoustic emissions, temperature, pressure, vibration) to yield a holistic evaluation of equipment condition. A new hybrid approach, where a deep learning-based autoencoder is employed for feature learning and a Random Forest classifier is used to detect anomalies and predict Remaining Useful Life (RUL), is suggested. The proposed framework is tested using a real-world industrial dataset and found to achieve considerable improvements in prediction accuracy and lower false alarm rates than conventional methods. The findings emphasize the promise of this methodology to maximize maintenance schedules, reduce downtime, and enhance overall industrial operation efficiency.
Optimizing Hybrid Renewable Energy Systems for Rural Electrification: A Multi-Criteria Decision-Making Approach with Enhanced Whale Optimization Algorithm
Cloud computing offers flexible and economical infrastructure, yet dynamic resource allocation is a significant problem owing to volatile workloads, disparate resources, and varied application needs. Static or rule-based solutions cannot be flexible, and machine learning methods commonly miss complex dependencies. Reinforcement Learning (RL) provides flexibility but is not scalable with large state spaces, and current graph-based solutions are heavily dependent on hand-crafted relations. To overcome these constraints, we suggest an end-to-end approach that combines RL and Graph Neural Networks (GNNs) for dynamic resource allocation. The cloud system is represented as a graph, where virtual machines, servers, and network devices are nodes and their dependencies are edges. The GNN captures complex representations of these dependencies, which are utilized by an RL agent to perform well-informed allocations. We compare our proposed GNN-RL framework with static, threshold-based, and independent RL methods using CloudSim. Our results show dramatic improvements in resource utilization, task execution time, and energy consumption. Our work identifies the feasibility of integrating GNN-RL to offer a scalable and flexible solution for resource management in clouds with potential to open up more intelligent and efficient cloud computing platforms.
Enhanced Intrusion Detection System for IoT Networks Using Hybrid Deep Learning and Feature Selection Techniques
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, making IoT networks prime targets for cyberattacks. Traditional security mechanisms often fall short in addressing the complexity and scale of these threats. This paper proposes an enhanced Intrusion Detection System (IDS) for IoT networks utilizing a hybrid deep learning approach combined with effective feature selection techniques. The proposed IDS integrates a Convolutional Neural Network (CNN) for feature extraction and a Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for temporal sequence analysis of network traffic. A feature selection method based on Information Gain and Variance Thresholding is employed to reduce dimensionality and improve the efficiency and accuracy of the deep learning model. The performance of the hybrid IDS is evaluated using benchmark IoT network datasets, demonstrating superior detection accuracy, lower false positive rates, and enhanced resilience against various attack vectors compared to existing state-of-the-art methods. The results highlight the potential of this approach to significantly improve the security posture of IoT environments.
Enhanced Predictive Maintenance Strategy for Industrial Robotics using Hybrid Deep Learning and Sensor Fusion
This paper presents an enhanced predictive maintenance (PdM) strategy for industrial robots utilizing a hybrid deep learning approach integrated with sensor fusion. The proposed methodology combines data from multiple sensors (vibration, current, temperature, and position encoders) to provide a comprehensive understanding of robot health. A novel deep learning architecture, consisting of a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency modeling, is employed to predict Remaining Useful Life (RUL) and detect anomalies. The performance of the hybrid model is compared against traditional machine learning algorithms and single deep learning models, demonstrating superior accuracy and robustness. The findings suggest that this integrated approach can significantly reduce downtime, optimize maintenance schedules, and improve the overall efficiency of industrial robotic systems. Furthermore, the practical implications of this research for smart manufacturing environments are discussed.
Optimizing Hybrid Intrusion Detection Systems Using Federated Learning and Explainable AI for Enhanced Network Security
The escalating sophistication and volume of cyberattacks demand robust and adaptable intrusion detection systems (IDSs). Traditional centralized IDSs often struggle with scalability, data privacy concerns, and the ability to detect novel attacks. This paper proposes a novel hybrid IDS framework that leverages federated learning (FL) and explainable AI (XAI) to overcome these limitations. The framework combines the strengths of signature-based and anomaly-based detection methods within a federated learning environment, allowing for collaborative model training across multiple network edge devices without sharing sensitive raw data. Furthermore, XAI techniques are integrated to provide insights into the IDS's decision-making process, enhancing transparency and trust. The effectiveness of the proposed approach is evaluated using a benchmark network intrusion dataset, demonstrating significant improvements in detection accuracy, reduced false positive rates, and enhanced model explainability compared to traditional centralized and non-federated IDS deployments. The results highlight the potential of FL and XAI to revolutionize network security by enabling decentralized, privacy-preserving, and interpretable intrusion detection.
A Hybrid Metaheuristic Optimization Approach for Enhanced Resource Allocation in Cloud Computing Environments
Cloud computing has emerged as a pivotal paradigm for delivering scalable and on-demand computing resources. Efficient resource allocation is paramount to maximizing the benefits of cloud infrastructure, including performance, cost-effectiveness, and energy efficiency. This paper presents a novel hybrid metaheuristic optimization approach that combines the strengths of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for enhanced resource allocation in cloud environments. The proposed algorithm, named GA-PSO, leverages the global exploration capabilities of GA and the local exploitation capabilities of PSO to achieve a superior balance between exploration and exploitation. The performance of the GA-PSO algorithm is evaluated through extensive simulations under various workload scenarios and compared against traditional GA and PSO algorithms. The results demonstrate that GA-PSO significantly improves resource utilization, reduces makespan, and minimizes energy consumption compared to its counterparts, highlighting its potential as a robust and efficient solution for resource allocation in cloud computing.
An Adaptive Hybrid Approach Leveraging Machine Learning and Optimization Techniques for Enhanced Energy Efficiency in Smart Grids
The smart grid represents a significant advancement in energy infrastructure, promising enhanced efficiency, reliability, and sustainability. However, realizing its full potential requires sophisticated control strategies capable of adapting to the dynamic and unpredictable nature of energy demand and supply. This paper presents a novel adaptive hybrid approach that combines machine learning (ML) techniques with optimization algorithms to improve energy efficiency in smart grids. The ML component leverages historical data and real-time sensor information to predict energy demand and renewable energy generation, while the optimization component uses these predictions to optimally allocate resources, schedule energy storage, and manage demand response programs. The proposed approach is evaluated through simulations using realistic smart grid scenarios, demonstrating significant improvements in energy efficiency, reduced peak demand, and enhanced integration of renewable energy sources compared to traditional methods. The results highlight the potential of the hybrid approach to address the challenges of modern energy management and contribute to a more sustainable and resilient energy future.