International Journal of Innovative Research in Technology and Science
Welcome to the International Journal of Innovative Research in Technology and Science (IJIRTS), ISSN - 2321-1156, a premier online platform that publishes groundbreaking ideas and research findings across diverse disciplines. As a peer-reviewed and open-access scholarly journal, IJIRTS is a pivot for global researchers, academics, and professionals contributing to advancing knowledge in Engineering, Science, Computer Science, and various emerging technologies. IJIRTS upholds rigorous peer review principles to ensure the published content's quality and credibility. Our dedication to open access empowers researchers from all institutions to freely access and utilize the wealth of knowledge embedded within our articles and papers. Through a comprehensive indexing policy, we facilitate easy accessibility for research scholars, fostering an environment conducive to staying abreast of the latest trends in science and technology. Distinguishing itself by actively promoting online publication, IJIRTS aligns with the evolving landscape of academic discourse. Embracing the guidelines and standards set by the Committee on Publication Ethics (COPE), IJIRTS strives to maintain the highest ethical standards across all phases of online publication in harmony with the principles outlined in the Budapest Open Access Initiative (BOAI). "IJIRTS" is a scholarly beacon, providing a platform for theoretical and practice-oriented papers addressing contemporary technological challenges. This journal is published bi-monthly in English and invites research contributions across all Engineering and Science disciplines (Computer Science, Information Technology, Information Science, Electrical Engineering, Electronics Engineering, and Mechanical Engineering). To enhance the quality of this journal, we extend invitations to authors for guest editorship, reviewers, and program participants. We recognize the invaluable role played by contributors in elevating the journal to greater heights, and we are committed to continuous improvement. IJIRTS is more than a mere publication; it is a dynamic community that thrives on exchanging innovative ideas. As part of our commitment to accessibility, all papers published in IJIRTS are freely available as online full-text content with permanent worldwide web links. Furthermore, our articles are indexed and accessible in major academic international databases, ensuring wide visibility and dissemination of research findings. IJIRTS welcomes researchers to submit their work for publication through the user-friendly online submission system of the journal. Join us in shaping the future of technology and science by contributing to the vibrant tapestry of knowledge at the International Journal of Innovative Research in Technology and Science.
Bio-Inspired Feature Selection and Deep Neural Networks for Accurate Diabetes Prediction
Accurate and early prediction of diabetes is critical for effective disease management and timely intervention. This study proposes a hybrid deep learning framework that integrates a bio-inspired feature selection technique with a deep neural network (DNN) classifier to enhance predictive performance. An improved Shuffled Frog Leaping Algorithm (SFLA) is employed to select the most informative features using a multi-objective fitness function combining classification accuracy and feature subset compactness. The optimised features are then input into a regularised DNN equipped with dropout layers and a softmax-based attention mechanism to improve generalisation and interpretability. Bayesian hyperparameter tuning and early stopping further refine the training process. The model is evaluated on the PIMA Indians Diabetes Dataset using stratified 10-fold cross-validation. The proposed method achieves 86.4% accuracy, 82.1% F1-score, and 0.91 AUC, outperforming traditional classifiers such as SVM, Random Forest, and standard DNNs. This approach provides a robust, scalable, and explainable framework for effective diabetes prediction in clinical environments.
Copyright (c) 2025 International Journal of Innovative Research in Technology and Science
A Survey on Reversible Data Hiding for Encrypted Image Based on Block Mean Difference Histogram Shifting
Reversible data hiding in encrypted images (RDHEI) has become a critical technique for safeguarding data privacy and security in the era of digital communication and cloud computing. This paper presents a comprehensive survey of existing reversible data hiding techniques, including least significant bit (LSB) substitution, difference expansion (DE), and histogram modification. These techniques have contributed significantly to the field by enabling the embedding and extraction of hidden data while preserving the reversibility of the original image. However, they often encounter challenges such as low peak signal-to-noise ratio (PSNR) and high error rates, which can degrade the visual quality and reliability of the reconstructed images. To overcome these limitations, we propose a novel RDHEI framework based on block mean difference histogram shifting. This approach leverages the block-wise mean differences and their histogram properties to embed additional data while ensuring high payload capacity and minimal image degradation. By effectively addressing the issues of low PSNR and high errors, our proposed technique significantly improves the performance of reversible data hiding in encrypted images. We compare the existing methods in detail, outlining their strengths and weaknesses, and highlight how our framework achieves lossless recovery of the original image. Ultimately, our proposed approach ensures secure, reliable, and reversible data embedding for a wide range of digital security applications.
A Convolutional Neural Network-Based Approach for High-Accuracy Fault Diagnosis in Photovoltaic Systems
The increasing demand for renewable energy has led to the widespread adoption of photovoltaic (PV) systems. However, the efficiency and reliability of these systems are often compromised due to various faults, which can significantly impact their performance. This paper presents a machine learning-based fault diagnosis system for solar panels, focusing on the detection and classification of faults in PV systems. We evaluate several machines learning models, including Random Forest, Decision Tree, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, and Naive Bayes, using a dataset of grid-connected PV system faults. Our results demonstrate that the Convolutional Neural Network (CNN) model outperforms other models, achieving a validation accuracy of 99.8%. The proposed system offers a robust solution for maintaining the efficiency and reliability of PV systems, contributing to the broader goal of sustainable energy development.
Sustainable Concrete Technology: Advancements in Accelerated Carbonation Curing
This research explores the advancements in carbon sequestration technologies for concrete production, emphasizing Accelerated Carbonation Curing (ACC) as a key sustainable approach. ACC integrates CO₂ utilization into concrete curing, improving mechanical properties, durability, and environmental efficiency. The study investigates how varied curing conditions and CO₂ pressures impact strength, durability, and permeability. A comparative analysis is conducted between traditional water curing and ACC. Findings demonstrate that ACC-treated concrete exhibits improved compressive strength, lower porosity, and greater resistance to environmental degradation. The paper also explores the role of supplementary cementitious materials (SCMs) and nano-additives in optimizing ACC.
Copyright (c) 2024 International Journal of Innovative Research in Technology and Science
Exploring the Use of Sustainable Materials in Self-Compacting Concrete: A Comprehensive Study on Performance, Durability, and Environmental Impact
This research paper investigates the incorporation of sustainable materials in self-compacting concrete (SCC) to improve its mechanical properties, durability, and environmental impact. The study focuses on the use of various industrial by-products and recycled materials, including fly ash, copper slag, palm oil fuel ash (POFA), and waste tire rubber (WTR), as partial replacements for conventional cement and aggregates in SCC. The experimental study evaluates key properties of SCC, such as compressive strength, workability, durability, and resistance to environmental challenges like acid attack and chloride penetration. Additionally, the environmental implications, including carbon footprint and waste reduction potential, are analyzed. The findings demonstrate that the incorporation of sustainable materials in SCC not only enhances the material’s performance but also contributes to an eco-friendlier construction approach.
Experimental Determination of Plastic Granules and Marble Powder on the Characteristics of Concrete
The incorporation of waste materials into concrete production provides an innovative solution to environmental and sustainability challenges. This study investigates the effects of Low-Density Polyethylene (LDPE) granules as a partial replacement for coarse aggregates and marble powder as a partial substitute for cement. Concrete mixes were prepared with plastic granules replacing coarse aggregates at 0%, 12%, 18%, 24%, 30%, 36%, 42%, 48%, 54%, and 60%, and marble powder replacing cement at 5%, 10%, and 15%. Compressive strength, split tensile strength, and workability were evaluated for all mixes. The results reveal that increasing the proportion of plastic granules reduces compressive strength and workability, while marble powder enhances compressive strength at lower replacement levels but decreases workability. This study provides insights into optimizing waste material use in concrete to balance sustainability and performance.
Analysis of Road Accidents on Highways in Madhya Pradesh: Trends, Contributing Factors, and Blackspot Identification
This study analyzes road accidents on highways in Madhya Pradesh from 2016 to 2023, highlighting the alarming rise in accident rates and fatalities. Using primary and secondary data, including field surveys, GIS mapping, and traffic records, the research identifies key contributing factors such as over-speeding, drunk driving, poor road infrastructure, and driver distractions. A significant portion of the accidents occurred on major highways like NH-44 and SH-27, with specific blackspots identified using GIS technology. Regression models were developed to predict accident likelihood based on factors like traffic volume, road quality, and weather conditions. The study found that two-wheelers were the most vulnerable vehicle type, with high fatality rates. The research underscores the need for improved road safety interventions, including enhanced speed enforcement, better road infrastructure, stronger drunk driving laws, and public safety awareness campaigns. The findings aim to guide policymakers in reducing road accidents and fatalities, ultimately improving highway safety in Madhya Pradesh. Future work includes refining predictive models and implementing real-time data collection systems for dynamic safety management.
Towards Safer Roads: A Framework for Analyzing and Improving Road Safety Performance
The objective of this research project is to develop a comprehensive framework for road safety performance indicators utilizing statistical modelling techniques. The framework is designed to assess road safety conditions, identify influential factors, and contribute to evidence-based interventions. Through the collection and analysis of data on road traffic accidents, infrastructure, traffic volume, and driver behaviour, statistical models are employed to unveil relationships between variables and road safety outcomes. The paper discusses the development process, application feasibility, and potential impacts on road safety strategies.
A KNN-Based Intrusion Detection System for Enhanced Anomaly Detection in Industrial IoT Networks
Anomaly detection in industrial Internet of Things (IIoT) networks is critical for ensuring robust security against evolving cyber threats. Traditional Intrusion Detection Systems (IDS) face challenges such as imbalanced data, sparse anomalies, and high false positive rates, which hinder their effectiveness. This study addresses these limitations by proposing a machine learning-based IDS leveraging the K-Nearest Neighbors (KNN) algorithm. The system efficiently identifies and classifies network anomalies by preprocessing large-scale data streams, selecting relevant features, and optimizing classification tasks. The proposed system demonstrates high precision (87.17%), recall (94.23%), F1-score (90.56%), and accuracy (92.76%) using publicly available intrusion detection datasets. These metrics indicate a significant improvement over existing methods, showcasing the model’s ability to adapt to varying network conditions and detect anomalies with minimal false alarms. This research contributes to enhancing IIoT security by providing a scalable, efficient, and reliable solution for real-time anomaly detection. The findings underline the importance of integrating advanced machine learning techniques into IDS frameworks, paving the way for more secure industrial environments. Future work may explore the integration of deep learning models and real-time deployment in dynamic IIoT ecosystems.
A Review on Dynamic Prioritization Strategy for Grid Support Using Plug-in Electric Vehicles
The increasing adoption of Plug-in Electric Vehicles (PEVs) and the integration of renewable energy sources into power grids necessitate dynamic strategies to optimize grid stability. This review explores cutting-edge dynamic prioritization strategies that manage PEV charging and discharging based on real-time grid conditions. Key findings highlight how advanced control systems, predictive algorithms, and decentralized communication protocols enable PEVs to function as distributed energy resources. These strategies enhance grid flexibility, facilitate demand response, and support peak load shaving while optimizing the integration of renewable energy. The study identifies significant challenges, including scalability, data security, and user participation, and suggests future directions such as leveraging machine learning, blockchain technology, and incentive programs. By addressing these challenges, dynamic prioritization strategies can unlock the full potential of PEVs, fostering a more resilient and sustainable energy system. This review provides critical insights for researchers, policymakers, and industry stakeholders aiming to advance PEV-grid integration.
Advancements and Challenges in Load Modeling for Plug-In Electric Vehicles: A Comprehensive Review
This review provides a comprehensive examination of Plug-in Electric Vehicles (PEVs), focusing on their current market status, technological advancements, and the challenges associated with their integration into the power grid. With increasing global emphasis on sustainable energy and carbon emission reduction, PEVs are emerging as pivotal in the transition toward cleaner transportation. This paper explores various load modelling techniques that predict and manage the electricity demand resulting from PEV adoption, highlighting their role in grid management and strategic energy planning. Key topics include uncontrolled charging, smart charging strategies, and Vehicle-to-Grid (V2G) systems, alongside their respective impacts on grid stability and renewable energy integration. The review identifies research gaps and underscores the importance of innovative load modelling methods to address the dynamic and scalable nature of PEV usage, ultimately contributing to a more sustainable energy future.
Enhanced Reversible Data Hiding Scheme for Encrypted Images Using BEHSM for Improved Quality and Security
The rise in digital media transfer has made data modification easier, necessitating secure communication and identity verification methods. Reversible data hiding (RDH), or lossless data hiding, allows for the lossless restoration of the original cover image after the extracted embedded information. Our study introduces a new method, BEHSM, which enhances the quality of encrypted images and allows for the lossless recovery of the secret image. Traditional RDH techniques, such as difference expansion and histogram modification, are common, but previous hierarchical embedding methods (HEM) were found to have low PSNR and high MSE. BEHSM, a reversible image transformation technique, improves upon these shortcomings by embedding secret data into a cover image with minimal distortion and ensuring the correct extraction of secret data while allowing for the recovery of the original cover image. Encryption is utilised before data hiding to address multimedia data transmission and storage security concerns. Our proposed BEHSM method demonstrates improved security, with low MSE and higher PSNR values, making it a robust solution for secure and lossless data hiding in encrypted images.
Reversible Data Hiding (RDH), Difference Expansion (DE), histogram modification, Image Encryption, Image Decryption, Image Recovery, PSNR, MSE, BEHSM.
Comprehensive Review of Soil Stabilization through Reinforcement Methods in Civil Engineering
This thorough review provides an in-depth analysis of soil stabilisation employing reinforcement techniques in civil engineering. The paper analyses the development of this method across time, highlighting its importance in contemporary building methods. The use of fibre-reinforced soil, geogrids, geotextiles, soil nailing, and other reinforcement techniques is covered in detail, emphasising their particular applications. We examine the effects of soil characteristics, geosynthetic characteristics, and environmental factors on reinforced soil stabilisation. The benefits and drawbacks of this strategy are also carefully analysed. This review is a great resource for engineers, researchers, and professionals on geotechnical research projects.
A Review of Recyclable Building Materials for Sustainability in India
Construction and demolition waste (C&DW) poses significant environmental challenges and resource scarcity concerns, particularly in rapidly growing economies like India. Despite generating an estimated 150 million tonnes of C&D waste annually, the country recycles just one percent of it, highlighting the urgent need for effective waste management strategies. This paper delves into the complexities of C&D waste management, exploring its sources, composition, and current practices in India. The study evaluates the magnitude of C&D waste generation, citing estimates ranging from 12 to 175 million tonnes per annum, underscoring the pressing demand-supply gap in construction materials. It examines the challenges of managing C&D waste, especially in smaller projects where waste segregation and reuse remain ambiguous. The paper proposes scalable solutions for effective waste management, drawing insights from successful international pilot projects. Furthermore, the paper outlines the composition of C&D waste, categorises it into major and minor components, and discusses the potential reuse of materials like concrete, bricks, tiles, wood, and metals. It emphasises the importance of recycling C&D waste to alleviate resource scarcity and reduce environmental impact. The paper also highlights current recycling practices in India, showcasing operational waste recycling plants across various cities. It discusses using recycled materials in construction projects and addresses regulatory barriers hindering the widespread adoption of recycling practices. In conclusion, the study advocates for proactive measures to minimise C&D waste generation and enhance recycling efforts. It underscores the crucial role of architects and designers in promoting sustainable construction practices and recommends integrating waste management plans into project management frameworks. India can transition towards a more sustainable and circular construction economy by fostering stakeholder collaboration and leveraging existing technologies.
A Review of Har Ghar Nal Yojana (Jal Jeevan Mission): A Case Study of 10 Villages
Access to portable tap water is necessary for every household, ensuring basic health and hygiene. Water supply schemes are actively being executed in rural areas of the state to provide clean drinking water. The Jal Jeevan Mission, initiated on August 15th, 2019, aims to furnish 55 litres of water per person per day to every rural household by 2024 through Functional Household Tap Connections (FHTC), ensuring consistent pressure on a regular and long-term basis in all rural households and public institutions. As of 2019, out of approximately 18.93 crore rural households, only about 3.23 crore (17%) were equipped with tap water connections, thus indicating the need to extend water supply to 83% of rural households. The program also mandates the implementation of source sustainability measures, including recharge and reuse through greywater management.
Unveiling Malicious Intent: A Theoretical Framework for Detecting Anomalies in Social Media Behaviours
The proliferation of social media platforms in business and politics has, unfortunately, facilitated the emergence of undesirable behaviours. From personal vendettas to corporate and political propaganda, engagement on social media has incentivised actions that are detrimental to individuals and society at large. The anonymity of hacked profiles has encouraged individuals to spread criticism and misinformation with impunity. This study delves into social media data to uncover the intricate relationships that underpin these problematic behaviours. Employing a sophisticated system, it identifies aberrant behaviours by detecting malicious or fraudulent accounts within social networks. Drawing on theories such as Influence, Homophily, and Balance Theory, a theoretical social framework is employed to enhance the accuracy of classifying potentially harmful users. Utilising metrics like the Jaccard coefficient, the system evaluates the similarity between user behaviours, incorporating graphical and linguistic cues to categorise end-users. The framework’s efficacy is rigorously assessed using standard parameters, including confusion matrices, to gauge performance. Additionally, the study recommends employing a friend connection identification framework for enhanced social atom anomaly detection, further fortifying efforts to combat undesirable behaviour on social media platforms.
Cognitive Computing in Healthcare: Applications, Advantages, Challenges, and Ethical Considerations
Cognitive computing emerges as a transformative catalyst as the healthcare industry experiences a fundamental shift towards data-driven and patient-centric models. This research paper investigates the profound influence of cognitive computing on the future landscape of healthcare. This paper delves into the applications, advantages, challenges, and ethical considerations associated with integrating cognitive computing into healthcare practices through a comprehensive examination of literature, case studies, and emerging trends. Drawing upon interdisciplinary perspectives, we explore how cognitive computing enhances diagnostic accuracy, optimises treatment strategies, enables personalised medicine, and enhances overall healthcare provision. Furthermore, the paper scrutinises the ethical ramifications of employing cognitive computing in healthcare, addressing concerns such as data privacy, bias, and the human-machine interface. By synthesising existing knowledge and proposing future research avenues, this paper contributes to the ongoing discourse on the pivotal role of cognitive computing in shaping the trajectory of healthcare.
Investigating Power-Aware Protocols for Wireless Sensor Network Durability
The paper reviews existing literature, analysing the strengths and limitations of different power-aware protocols deployed in WSNs. It explores how these protocols impact overall network durability, considering energy efficiency, communication overhead, and adaptability to dynamic environmental conditions. Furthermore, the research assesses the practical implications of implementing power-aware protocols in real-world scenarios, offering insights into their performance, reliability, and potential challenges. Case studies and experimental results are presented to illustrate the impact of these protocols on the longevity and sustained functionality of wireless sensor networks. The findings of this investigation contribute valuable knowledge to the field, offering guidance for researchers, engineers, and practitioners seeking to optimise power-aware protocols for prolonged durability in wireless sensor networks.
Multilevel Inverter-Based Smart Grid IoT Integration: Enhancing Power Quality and Energy Efficiency
This research paper explores the integration of multilevel inverters into smart grid systems intending to enhance power quality and energy efficiency. The increasing deployment of Internet of Things (IoT) technologies in smart grids provides an opportunity to optimise energy management and improve overall system performance. The proposed multilevel inverter system leverages its advanced voltage control capabilities to address power quality issues, such as harmonics and voltage sags, commonly encountered in traditional power distribution networks. Through the integration of IoT in the smart grid, real-time monitoring, control, and communication capabilities are harnessed to enable intelligent decision-making. This paper investigates the synergy between multilevel inverters and IoT technologies, highlighting the benefits of their combined implementation. The research delves into the adaptive control strategies enabled by the IoT, facilitating dynamic adjustments in response to grid conditions and demands. Furthermore, the study assesses the impact of this integrated system on energy efficiency, examining the potential for optimised power flow and reduced losses. The results presented in this paper contribute valuable insights into the practical implementation of multilevel inverter-based smart grid IoT integration, offering a comprehensive understanding of its implications for power quality enhancement and energy efficiency improvement in modern electrical networks.
Exploring Microbial Spoilage in Fruits: Causes, Detection Methods, and Preventive Measures
This review paper explores the intricate dynamics of microbial spoilage in fruits, delving into its causes, detection methods, and preventive measures. It examines how various microorganisms contribute to fruit spoilage, from bacteria and fungi to yeasts, and elucidates the biochemical processes underlying their actions. Additionally, it discusses diverse techniques for detecting microbial spoilage, encompassing traditional and modern approaches such as sensory evaluation, microbiological assays, and molecular methods. Furthermore, the paper highlights preventive strategies against microbial spoilage, encompassing pre-harvest and post-harvest interventions and technological advancements like modified atmosphere packaging and antimicrobial agents. By synthesising current knowledge in this field, this review aims to provide a comprehensive understanding of microbial spoilage in fruits, offering insights for researchers, industry professionals, and consumers.
Enhancing Uplink Communication with Multi-User Detection in NOMA Through Deep Neural Networks
In the realm of wireless communication, Multi-user Detection (MUD) techniques have become crucial for ensuring efficient and reliable transmission in complex network scenarios. Particularly in the uplink channel of Non-Orthogonal Multiple Access (NOMA) systems, effective MUD approaches are essential to overcome interference and enhance overall performance. One innovative solution that has shown promise in addressing these challenges is the utilisation of Deep Neural Networks (DNNs) for MUD in grant-free NOMA uplink communications. By leveraging the power of artificial intelligence and machine learning, DNNs can effectively distinguish and decode signals from multiple users sharing the same frequency band in NOMA networks. By training deep learning algorithms on large datasets of multi-user signals, DNNs can learn complex patterns and correlations, enabling them to accurately separate and detect individual user signals in the presence of interference. This capability improves communication’s overall reliability and efficiency in NOMA systems and opens up opportunities for enhancing spectral efficiency and capacity.
Navigating the Dynamics of Online Consumer Behaviour: Insights for E-Commerce Strategies in India and Globally
Consumer behaviour in online marketplaces is a dynamic field that significantly impacts e-commerce strategies. This paper explores consumer behaviour patterns in both India and the global context. We delve into factors driving online shopping trends, marketplaces' role, and digitalisation's impact. Understanding consumer behaviour in online marketplaces is critical for businesses to attract and retain customers effectively, increase sales, and compete successfully in the digital economy. This research paper aims to provide a comprehensive analysis of consumer behaviour in online marketplaces through a review of existing literature and insights into the implications for management strategies based on the results and findings of the research.
Study of Phosphate Metabolism of cyanobacterium Leptolyngbya
Leptolyngbyais is a polyextremophile found in more than one extreme condition. It thrives in lukewarm water and grows readily in high salinity. This cyanobacterium gains considerable importance, considering recent studies about halophilic enzymes and proteins. In the present study, some important conclusions will take us a long way in formulating principles for the beneficial use of this extreme organism. The high phosphatase activity rate indicates the enzyme’s flexibility and stability at high temperatures and salinity. Phylogenetic studies with 16S rDNA show some very interesting results in Leptolyngbya. The study suggests that it evolved from a common ancestor and diverged into discrete species in dissimilar habitats separated by great distances geographically. The study has unravelled a lot of significant data and opened many new avenues of research in the industrial use of extremophiles, which answer the fundamental questions of the existence of extremophilic organisms on Earth and their use in various biotechnological processes.
Exploring the Role of Epigenetic Modifications in Criminal Behavior: Implications for Prevention and Intervention
Epigenetic modifications are alterations to DNA that impact how cells read genes and control their expression or activity but do not alter the sequence of DNA’s building blocks. These changes can be hereditary or can be reversible. This field is being worked on extensively. Violent and stressful environments have long-term, potentially generational effects on brain development and genes. Factors that influence epigenetic changes are diet, lifestyle, smoking, alcohol, environmental pollution and psychological stress. The bio-psycho-social processes of epigenesis illuminate the mechanism of potential causes of criminal behaviour. Most long-term offenders were raised in low-income, maltreated, and neglected environments. In this regard, research on the epigenetic effects of crime holds great promise for improving our knowledge of crime and violence and bringing forth fresh ideas for crime prevention.
Advancements in AI-driven Techniques for MIMO-OFDM Systems: A Comparative Analysis
This study systematically reviews and compares various AI-driven approaches employed in MIMO-OFDM systems, including machine learning algorithms, neural networks, and optimization techniques. It delves into each method’s strengths, limitations, and unique contributions, offering insights into their effectiveness in addressing specific challenges such as channel estimation, interference mitigation, and spectral efficiency improvement. Furthermore, the paper discusses the impact of AI on system adaptability and robustness in dynamic communication environments. Through a comparative lens, the research aims to identify trends, best practices, and areas of improvement in AI applications for MIMO-OFDM systems. The findings contribute to a deeper understanding of the evolving landscape of AI-driven enhancements in communication systems, offering valuable insights for researchers, engineers, and practitioners in the field.
A Review of Enhanced Index Price Movement Prediction Using Ensemble Deep Learning Models
This review paper investigates and evaluates advancements in index price movement prediction through ensemble deep learning models. Focusing on the fusion of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), the study comprehensively analyses their collective impact on enhancing the precision and accuracy of index price forecasts in financial markets. The paper reviews and compares various ensemble strategies employed in deep learning, assessing their effectiveness in capturing complex patterns and dependencies within financial data. Special attention is given to the synergistic integration of CNN and DNN, highlighting how their combined capabilities contribute to improved predictive performance. Through an extensive literature review, the study examines the methodologies, architectures, and training strategies associated with ensemble deep learning models in the context of index price prediction. Additionally, the paper explores ensemble techniques’ implications for mitigating overfitting and enhancing model robustness in dynamic financial environments.
Advancements in Computer Networking: A Comprehensive Overview of Emerging Technologies, Protocols, and Trends
Computer networking has undergone significant advancements in recent years, revolutionizing communication and connectivity across the globe. This abstract provides a comprehensive overview of the key developments in computer networking, focusing on emerging technologies, protocols, and trends. It examines the evolution of networking architectures from traditional wired networks to modern wireless and cloud-based infrastructures. Furthermore, it explores the proliferation of Internet of Things (IoT) devices and their impact on network scalability, security, and management. Additionally, this research delves into the role of software-defined networking (SDN) and network function virtualization (NFV) in enhancing network flexibility and efficiency. Moreover, it discusses the rise of edge computing and its implications for reducing latency and improving user experience. Security challenges and solutions in contemporary networking environments, including encryption, authentication mechanisms, and threat detection techniques, are also addressed. Finally, the abstract highlights future directions and potential research areas in computer networking, emphasizing the need for innovative solutions to address the evolving demands of the digital age.
Visionary Assistance: Integrating Ultrasonic and GPS Technologies for the Visually Impaired
This paper presents a groundbreaking solution to improve the lives of visually impaired individuals. This smart device utilizes ultrasonic sensors, GPS technology, fire sensors, and location tracking to provide comprehensive support for the visually impaired. The device emits ultrasonic waves and uses GPS to enable obstacle detection, real-time navigation, and location sharing with family members. The inclusion of fire sensors enhances safety by alerting users to potential fire hazards, even when they may not perceive them. The project focuses on affordability and efficiency, aiming to make this technology accessible to many users. With an estimated 39 million blind individuals globally, the potential impact of this device is significant. Its implementation offers visually impaired users increased comfort, speed, and confidence in navigation, promoting greater independence and participation in various aspects of life. The E-Navigation smart device for the visually impaired represents an innovative and promising development with the potential to improve the lives of millions of people facing visual impairment challenges.
Automatic Vehicle Defect Detection App: AI-Powered Servicing Prioritisation
This research paper introduces an AI-powered Automatic Vehicle Defect Detection App designed to identify potential vehicle defects before servicing. Traditional inspection methods are often time-consuming and prone to human error. Leveraging machine learning algorithms for this app streamlines inspections, enhances accuracy, and ensures timely defect detection. The paper outlines the app’s architecture, data collection, training, evaluation metrics, usability, efficiency, and impact on the automotive industry.
SmartGarb: An AI-Powered Application for Predicting Garbage Collection Vehicle Arrival Time in Small Cities with Office Commute Constraints
This research paper introduces SmartGarb, a pioneering mobile application designed to address the persistent issue of unpredictable garbage collection systems that manage vehicle arrival times in small cities, particularly affecting office commuters. Leveraging artificial intelligence (AI), Global Positioning System (GPS), and machine learning (ML), SmartGarb predicts the precise arrival time of garbage collection vehicles, enabling residents to plan their schedules more effectively and reduce inconvenience. The paper presents the development, implementation, and evaluation of SmartGarb, highlighting its potential to enhance urban waste management systems and improve the quality of life for city residents.