Computer and Information Science
A Prospective Metaverse Paradigm Based on the Reality-Virtuality Continuum and Digital Twins
After decades of introducing the concept of virtual reality the expansion and significant advances of technologies and innovations such as 6g edge computing the internet of things robotics artificial intelligence blockchain quantum computing and digital twins the world is on the cusp of a new revolution. By moving through the three stages of the digital twin digital native and finally surrealist the metaverse has created a new vision of the future of human and societal life so that we are likely to face the next generation of societies (perhaps society 6) in the not too distant future. However until then the reality has been that the metaverse is still in its infancy perhaps where the internet was in 1990. There is still no single definition few studies have been conducted there is no comprehensive and complete paradigm or clear framework and due to the high financial volume of technology giants most of these studies have focused on profitable areas such as gaming and entertainment. The motivation and purpose of this article are to introduce a prospective metaverse paradigm based on the revised reality-virtuality continuum and provide a new supporting taxonomy with the three dimensions of interaction immersion and extent of world knowledge to develop and strengthen the theoretical foundations of the metaverse and help researchers. Furthermore there is still no comprehensive and agreed-upon conceptual framework for the metaverse. To this end by reviewing the research literature discovering the important components of technological building blocks especially digital twins and presenting a new concept called meta-twins a prospective conceptual framework based on the revised reality-virtuality continuum with a new supporting taxonomy was presented.
Extensive Review of Literature on Explainable AI (XAI) in Healthcare Applications
Artificial Intelligence (AI) techniques are widely being used in the medical fields or various applications including diagnosis of diseases prediction and classification of diseases drug discovery etc. However these AI techniques are lacking in the transparency of the predictions or decisions made due to their black box-type operations. The explainable AI (XAI) addresses such issues faced by AI to make better interpretations or decisions by physicians. This article explores XAI techniques in the field of healthcare applications including the Internet of Medical Things (IoMT). XAI aims to provide transparency accountability and traceability in AI-based systems in healthcare applications. It can help in interpreting the predictions or decisions made in medical diagnosis systems medical decision support systems smart wearable healthcare devices etc. Nowadays XAI methods have been utilized in numerous medical applications over the Internet of Things (IoT) such as medical diagnosis prognosis and explanations of the AI models and hence XAI in the context of IoMT and healthcare has the potential to enhance the reliability and trustworthiness of AI systems.
Patent Selections
Preface
Graphical User Interface for Handwritten Mathematical Expression Employing RNN-based Encoder-decoder Model
Scientific technical and educational research domains all heavily rely on handwritten mathematical expressions. The extensive use of online handwritten mathematical expression recognition is a consequence of the availability of strong computational touch-screen appliances such as the recent development of deep neural networks as superior sequence recognition models.
Further investigation and enhancement of these technologies are vital to tackle the contemporary obstacles presented by the widespread adoption of remote learning and work arrangements as a result of the global health crisis.
Handwritten document processing has gained more attention in the last ten years due to notable developments in deep neural network-based computer vision models and sequence recognition as well as the widespread proliferation of touch and pen-enabled smartphones and tablets. It comes naturally to people to write by hand in daily interactions.
In this article authors implemented Hand written expressions using RNN-based encoder for the CROHME dataset. Later the proposed model was validated using CNN-based encoder and end-to-end encoder decoder techniques. The proposed model is also validated on other datasets.
The RNN-based encoder model yields 82.78% while the CNN-based encoder model and end-to-end encoder-decoder technique results in 81.38% and 80.73% respectively.
1.6% accuracy improvement was attained over CNN-based encoder while 2.4% accuracy improvement over end-to-end encoder-decoder. CROHME dataset 2019 version results in better accuracy than other datasets.
Role of Artificial Intelligence in VLSI Design: A Review
Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design notably in the design of digital and analogue integrated circuits (ICs) to address challenges such as rising networked devices aggressive time-to-market and ever-increasing design complexity. However very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the state-of-the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats including unstructured semi-structured and structured can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single cohesive environment for creation as was mentioned in the section that came before this one. Consequently the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.
An Improved Aquila Optimizer with Local Escaping Operator and Its Application in UAV Path Planning
With the development of intelligent technology Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end.
In order to obtain a better flight path this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator named LEOAO to deal with the UAV path planning problem in three-dimensional environments.
UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance flight height turning angle and climbing/diving angle. In this paper the LEOAO is introduced to find the optimal path by minimizing the cost function and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm.
To test the performance of LEOAO two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer whale optimization algorithm including the original Aquila optimizer.
The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally the improved Aquila optimizer obtains a better path.
A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System
Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing edge computing is provided as a new paradigm to effectively reduce processing latency energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems an intelligent task migration assignment mechanism based on blockchain is proposed which jointly considers the factors of resource allocation resource control and credit degree.
In this paper an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay energy consumption communication and credit degree. Here the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge.
Compared to the existing two benchmark mechanisms the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost.
To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios an intelligent blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively a dual “actor-reviewer” neural network algorithm is designed and executed.
Deep Neural Network Framework for Predicting Cardiovascular Diseases from ECG Signals
Cardio Vascular Disease (CVD) a primary cause of death worldwide includes a variety of heart-related disorders like heart failure arrhythmias and coronary artery disease (CAD) where plaque buildup narrows the heart muscle's blood vessels and causes angina or heart attacks. Genetics congenital anomalies bad diet lack of exercise smoking and chronic diseases including hypertension and diabetes can cause cardiac disease.
The symptoms can range from chest pain and shortness of breath to exhaustion and palpitations and diagnosis usually involves a medical history physical examination and electrocardiograms (ECGs) and stress testing. Lifestyle adjustments medicines angioplasty and bypass grafts or heart transplants are possible treatments. Preventive measures include healthy living risk factor management and frequent checkups which are few measures whereas advanced algorithms can analyze massive volumes of ECG and MRI data to find patterns and anomalies that humans may overlook.
The deep learning models increase arrhythmia coronary artery disease and heart failure diagnosis accuracy and speed. They enable predictive analytics early intervention and personalized treatment programs increasing cardiac care results. The proposed DNN model consists of a 3-layer architecture having input hidden and output layers. In the hidden layer 2 layers namely the dense layer and batch normalization layer are added to enhance its accuracy.
Three different optimizers namely Adam AdaGrad and AdaDelta are tested on 50 epochs and 32 batch sizes for predicting cardiovascular disease. Adam optimizer has the highest accuracy of 85% using the proposed deep neural network.
Web-based Vulnerability Analysis and Detection
Introduction: In today’s digital world protecting organizations from breaches hacking data theft and unauthorized access is key. Web-based vulnerability analysis and detection is a big part of that. Method: This research introduces a new approach to web-based vulnerability assessment by combining advanced automated tools with human expertise a complete way to identify rank and fix critical vulnerabilities in web applications and websites. Our research presents a new automated scanner built with Python and Selenium which can detect a wide range of vulnerabilities including SQL injection cross-site scripting (XSS) and emerging threats. The tool’s modular architecture and regular expression-based detection methods allow for flexibility and speed in detecting common and uncommon vulnerabilities. We propose a framework for vulnerability ranking so organizations can prioritize their fix efforts. Our approach considers exploiting potential severity and patch availability to give a more accurate risk assessment. Through real-world web application testing we demonstrate the effectiveness of our approach in detecting and fixing vulnerabilities. Result: Our results show significant improvement in detection accuracy and speed compared to traditional methods especially for complex and dynamic web applications. This research adds to the body of knowledge in web security and vulnerability management by combining advanced automated scanning with human expertise. Conclusion: Our findings provide practical advice for organizations looking to improve their cybersecurity in the ever-changing digital world.
An Optimized Transmission Mechanism for Mitigating Jamming Attacks in Multi-Hop Wireless Networks
To address the vulnerability of Multi-Hop Wireless Network Systems (MHWNs) to jamming attacks and propose an effective solution to maintain communication integrity and Quality of Service (QoS).
In MHWNs the open-access nature makes them susceptible to jamming attacks which disrupt communication by interfering with authenticated nodes in the wireless medium. Existing methods primarily focus on tracking and countering jammers but lack effectiveness in preventing communication disruptions.
The objective of this study is to introduce a novel algorithm Optimized Transmission Mechanism (OTM) to mitigate the impact of jamming attacks on MHWNs. OTM aims to optimize node handover and packet routing to bypass jammed areas ensuring uninterrupted packet transmission and preserving QoS.
The proposed OTM algorithm determines the optimal transmission route based on radio transmitter location and connection quality. It prioritizes routes with the highest connection quality to maintain QoS even in jammed conditions. Additionally it incorporates mechanisms for packet redirection away from jammed areas to ensure successful transmission.
Evaluation of the Extended Optimized Transmission Mechanism (EOTM) demonstrates significant improvements in packet delivery performance compared to existing algorithms. The enhanced algorithm effectively mitigates the impact of jamming attacks ensuring reliable communication and preserving QoS in MHWNs.
The proposed OTM algorithm presents a promising approach to counter-jamming attacks in MHWNs by dynamically routing packets to avoid jammed areas and maintain communication integrity. The results highlight the effectiveness of EOTM in improving packet delivery performance and ensuring uninterrupted communication in the face of jamming threats.
Advanced Digital Technologies for Promoting Indian Culture and Tourism through Cinema
Culture and Tourism are two mainly interrelated elements that contribute a lot to achieving Sustainable Development for any developing country especially India which has an extremely rich historical and cultural background. Tourism Industry is the fastest growing sector in a local economy creating several job opportunities which ultimately raise the standard of living of people which further raises the consumption level of goods and services resulting in a rise in the Gross Domestic Product (GDP) of a country. However various studies pointed out major promotional strategies concerning tourism and culture but an amalgamated promotional approach for both was still missing. With this motivation the current study aims at providing an amalgamated promotional approach in assimilation with the latest Industry 4.0 technologies such as Artificial Intelligence (AI) Machine Learning (ML) Big Data Blockchain Virtual Reality (VR) Digital Twin and Metaverse to the Indian tourism industry by reviewing prior research studies. The findings of the current study are establishing an online future travel demands forecasting system an online tourists’ destination personalized recommendation system an online tourist’s review analysis recommendation system and an online destination image recommendation system and provide the practical design for it through 1+5 Architectural Views Model and by applying several ML algorithms such as CNN BPNN SVM Collaborative Filtering K-means Clustering API Emotion and Naïve Bayes algorithms. Finally this study has discussed challenges and suggested vital recommendations for future work with the assimilation of Industry 4.0 technologies.
Fermatean Fuzzy MOORA-based Approach for Hazard Analysis in An Aluminium Company
Hazard analysis as one of the main study subjects in ergonomics and occupational health and safety (OHS) risk assessment is a critical requirement for ensuring the health and safety of workers in work environments. Current hazard analysis approaches in the literature may have some shortcomings. This study aims to provide more reliable assessments in the hazard analysis process by overcoming the shortcomings of classical approaches.
This study proposes a new hazard analysis approach based on the integration of the Fermatean Fuzzy set and the multi-criteria decision-making (MCDM) method MOORA.
The proposed approach is used to perform a hazard analysis of a company operating in the metal industry which is one of the sectors where occupational accidents and occupational diseases most often occur. As a result of the application the hazards and associated risks in the aluminum metal company are prioritized.
This study provides an advanced risk assessment technique for ergonomists and OHS professionals to make better decisions in hazard analysis studies.
A Multivariate Intuitionistic Fuzzy Grey Model for Forecasting Electricity Consumption
Whether in the short medium or long term forecasting electricity consumption has always been an essential study area. In the literature many methods are used for future forecasting and are being improved daily to achieve better results.
The main objective of this study is to make the most accurate long-term electricity consumption forecast which is the basis for optimal future planning in the energy sector. Electric consumption forecasting is performed regionally since planning at the regional level is essential for more precise planning.
There may be different variables that affect electricity consumption. This study extends the multivariate grey model for electricity consumption prediction to intuitionistic triangular fuzzy numbers for nine regions. In the grey model population export and gross domestic product variables were used as independent variables and future predictions for these variables were obtained through the univariate intuitionistic triangular fuzzy grey model.
The results of the proposed method are compared with those of the classical univariate grey model univariate intuitionistic triangular fuzzy grey model and classical multivariate grey model. The results show that the error values of the proposed method are lower.
The study contributes to the development of the grey model. More accurate prediction results are obtained with the proposed method compared to similar methods
Lightweight Research on Fatigue Driving Face Detection Based on YOLOv8
With the rapid development of society motor vehicles have become one of the main means of transportation. However as the number of motor vehicles continues to increase traffic safety accidents also continue to appear bringing serious threats to people's lives and property safety. Fatigue driving is one of the important causes of traffic safety accidents.
To address this problem a target detection algorithm called VA-YOLO is designed to improve the speed and accuracy of facial recognition for fatigue checking. The algorithm employs a lightweight backbone network VanillaNet instead of the traditional backbone network which reduces the computational and parametric quantities of the model. The SE attention mechanism is also introduced to enhance the model's attention to the target features which further improves the accuracy of target detection. Finally in terms of the bounding box regression loss function the SIoU loss function is used to reduce the error.
The experimental results show that compared toYolov8n the VA-YOLO algorithm improves the accuracy by 1.3% while the number of parameters decreases by 30%.
This shows that the VA-YOLO algorithm has a significant advantage in realizing the balance between the number of parameters and accuracy which is important for improving the speed and accuracy of fatigue driving detection.
Computer-aided Drug Design: Innovation and its Application in Reshaping Modern Medicine
Computer-aided drug design has revolutionized the landscape of drug discovery accompanied by a new era of innovation and efficiency of novel therapeutic agents. This article review explores the diverse innovations and practical applications that have propelled CADD into the forefront of modern medicine. CADD a multidisciplinary field at the interaction of biology chemistry and computational science offers a toolkit for the identification and development of pharmaceutical compounds. It has the ability to predict molecular interaction between drug and biological targets with remarkable precision reducing the dependency on laborious and costly laboratory experiments. The review deals with two primary domains of CADD: structure-based and ligand-based design. This three-dimensional protein structure and screening of chemical libraries have led to rational changes. The analysis of known drug compounds' chemical and biological properties has enabled the creation of predictive models opening new routes for drug discovery. The impact of CADD on the pharmaceutical industry is clear. This review highlights its instrumental role in the development of antiviral agents cancer therapeutics and treatment for various diseases. The transformation potential of CADD is not without challenges including the need for substantial computational resources and the essential requirement of experimental analysis. The synergy between innovation and practical application is clear driving unexpected efficiency and precision in the identification of therapeutic solutions. As pharmaceutical research continues to evolve the role of CADD remains pivotal assuring the rapid translation of scientific innovation into real-world medical advancements.
Convolutional Neural Network-based Smart Disaster Management Framework for Real-time Detection and Management of Forest Fires
Timely detection of catastrophic natural disasters such as forest fires is critical to minimizing losses and ensuring rapid response. Artificial intelligence is increasingly being recognized as a valuable tool in enhancing various stages of disaster management.
This paper presents the development of a smart framework utilizing machine learning techniques for real-time detection and monitoring of natural disasters specifically forest fires. The proposed approach employs a 10-layer convolutional neural network (CNN) that classifies aerial images into Fire Non-Fire and Smoke categories with high precision and speed. In addition to this a CNN-based feature extraction process is performed and integrated with various ML classifiers including support vector machine k-nearest neighbor decision tree random forest and extra trees.
Extensive performance analysis reveals that the proposed 10-layer CNN model outperforms other classifiers achieving an accuracy of 97.64% in the binary classification of fire vs. non-fire and 95.61% in the three-class classification of Fire Non-Fire and Smoke classes. Furthermore a comparative study with existing state-of-the-art methods demonstrates the proposed model's superior performance in both accuracy and computational complexity.
These results demonstrate the potential of the proposed CNN-based framework to serve as a reliable and effective tool for real-time disaster management across various applications providing valuable support to emergency response teams in mitigating the impact of natural disasters.
An Enhanced Lightweight Secure Authentication and Privacy-Preserving Approach for VANETs
Vehicular-Adhoc-Networks (VANETs) have gained a lot of attention in the past ten years. Used extensively in intelligent transport systems (ITS) it facilitates the sharing of traffic data between vehicles and their immediate surroundings resulting in a more pleasant driving experience. When it comes to VANET security privacy and security are the two biggest obstacles. To improve security in VANET authentication and privacy-preserving methods are required because any specific disclosure of vehicle specifics including route data might have devastating consequences.
In light of this need this research introduces a novel framework for VANETs called enhanced timed efficient stream loss-tolerant authentication (ETESLTA) which enables a new kind of lightweight authentication while simultaneously protecting users' privacy. Initialisation registration mutual authentication broadcasting and verification and vehicle revocation are all parts of the suggested model. Furthermore the ETESLTA method requires very little memory while yet providing excellent broadcast authentication just like TESLTA. In addition the ETESLTA method incorporates a cuckoo filter to record the real details of cars inside the RSU's detection range.
The suggested approach provides strong anonymity to achieve privacy and resists common assaults and it features lightweight mutual authentication between the parties. A wide scale of experiments are conducted and the outcomes are evaluated in terms of numerous metrics to ensure the ETESLTA technique performs well.
The experimental results demonstrated that the ETESLTA method was superior to the most current state-of-the-art approaches.
BLE-IOT-PID: Bluetooth Low Energy (BLE) Based IOT Controlled PID Controller for Multi-loop Pilot Plant with Quantum Firefly PSO Optimization
Conventional Proportional–integral–derivative (PID) controls for multi-loop pilot plants are constrained by wired connections outdated control techniques and inefficient real-time data sensing and acquisition. As a result inefficiencies arise where control loops for parameters like temperature level and flow require continuous dynamic adjustments and precise regulation. To overcome this issue a full wireless solution is proposed which is the need of today’s era.
This study presents a novel PID controller for a multi-loop pilot plant utilizing a Bluetooth Low Energy (BLE) based Internet of Thing (IoT) system for wireless real-time data sensing and control. The system gathers data through sensors and sends it to cloud storage via a BLE access point which is then monitored using a mobile app called BIP. In addition to monitoring the BIP app serves as a control interface allowing PID parameters to be adjusted through a Quantum Firefly-Particle Swarm Optimization (QFPSO) algorithm integrated with the ThingSpeak cloud. This enables the control module to function in three distinct modes for the plant’s loops. Users can manually configure PID parameters as well as temperature and level set-points while the system automatically regulates the flow set-point based on real-time data. The BLE-based IoT system comprises five modules using Arduino Nano 33 BLE: a Flow Sensor a Temperature Sensor a Level Sensor IoT communication and an access point. These modules provide more accurate data than traditional sensing systems.
Key benefits of the proposed system include wireless accessibility user-friendliness a simplified design ease of upgrades and consistent control across multiple loops. The proposed system can be easily adapted for various types of industrial control systems with minimal effort.
Additionally the developed wireless sensor node can replace wired sensor nodes in any electronic system.
Field Pest Detection via Pyramid Vision Transformer and Prime Sample Attention
Pest detection plays a crucial role in smart agriculture; it is one of the primary factors that significantly impact crop yield and quality. Objective: In actual field environments pests often appear as dense and small objects which pose a great challenge to field pest detection. Therefore this paper addresses the problem of dense small pest detection.
We combine a pyramid vision transformer and prime sample attention (named PVT-PSA) to design an effective pest detection model. Firstly a pyramid vision transformer is adopted to extract pest feature information. Pyramid vision transformer fuses multi-scale pest features through pyramid structure and can capture context information of small pests which is conducive to the feature expression of small pests. Then we design prime sample attention to guide the selection of pest samples in the model training process to alleviate the occlusion effect between dense pests and enhance the overall pest detection accuracy.
The effectiveness of each module is verified by the ablation experiment. According to the comparison experiment the detection and inference performance of the PVT-PSA is better than the other eleven detectors in field pest detection. Finally we deploy the PVT- PSA model on a terrestrial robot based on the Jetson TX2 motherboard for field pest detection.
The pyramid vision transformer is utilized to extract relevant features of pests. Additionally prime sample attention is employed to identify key samples that aid in effectively training the pest detection models. The model deployment further demonstrates the practicality and effectiveness of our proposed approach in smart agriculture applications.