Article
Automation & Control Systems
Zhenzhen Jin, Deqiang He, Zexian Wei
Summary: In this paper, a weak fault diagnosis method for train axle box bearings is proposed based on parameter optimization Variational Mode Decomposition (VMD) and improved Deep Belief Network (DBN). By optimizing algorithm parameters and extracting fault feature information, the diagnostic accuracy of the bearings can be improved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, Son Vu Truong Dao
Summary: This paper proposes a two-stage model for fruit recognition using camera images, employing feature extraction and feature selection methods, and comparing the performance of several machine learning classifiers. The experimental results show significant improvements in accuracy and training time for the proposed model.
Article
Computer Science, Information Systems
Nur Lyana Shahfiqa Albashah, Helmi Md Rais
Summary: This study explores the effect of using a population factor to initialize the population for features selection and proposes a method that outperforms existing methods in terms of reducing feature numbers and error rates.
Article
Computer Science, Information Systems
Ranya Al-Wajih, Said Jadid Abdulkadir, Norshakirah Aziz, Qasem Al-Tashi, Noureen Talpur
Summary: The study introduces a memetic method HBGWOHHO that improves search algorithm performance by balancing exploration and exploitation. The proposed method outperforms other algorithms in enhancing accuracy and selecting fewer features in shorter computational time.
Article
Computer Science, Artificial Intelligence
Zhang Li
Summary: This paper proposes an improved Grey Wolf Optimization algorithm (OGGWO) for feature selection. OGGWO algorithm utilizes local opposing learning mapping to enhance convergence speed and population diversity, and combines the golden sine algorithm with grey wolf optimization algorithm to control search direction and distance. Experimental results on 18 standard datasets demonstrate the effectiveness and superiority of the OGGWO algorithm in improving classification accuracy and feature selection.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Preeti, Kusum Deep
Summary: This paper proposes a Random Walk Grey Wolf Optimizer based on dispersion factor (RWGWO) approach to address the feature selection problem in chronic disease. Experimental results show that the method performs well in reducing the number of features and improving the classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jianhua Jiang, Ziying Zhao, Yutong Liu, Weihua Li, Huan Wang
Summary: This paper proposes an improved Grey Wolf Optimizer algorithm (DSGWO) to address the issues of poor population diversity and weak global search capability in the original GWO algorithm. DSGWO significantly improves the algorithm's performance through the combination of group-stage competition mechanism and exploration-exploitation balance mechanism, and its applicability and effectiveness are demonstrated through experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Environmental
Jujie Wang, Wenjie Xu, Jian Dong, Yue Zhang
Summary: This study proposes an air pollutant prediction and early warning framework that combines feature extraction techniques, feature selection methods, and intelligent optimization algorithms to accurately predict and warn PM2.5 concentrations. Empirical results show that the proposed framework outperforms other comparative models in terms of prediction accuracy, warning accuracy, and prediction stability, making it an effective tool for air pollutant prediction and early warning.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Computer Science, Information Systems
Asha Sukumaran, Thomas Brindha
Summary: This paper introduces a new method for face shape classification using intelligent approaches, including face detection, pre-processing, feature extraction, and classification. The hybrid classifier combining Convolutional Neural Network (CNN) and Neural Network (NN) is utilized to classify face shapes into five categories: heart, oblong, oval, round, and square.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Spectroscopy
Mohamed B. El-Zeiny, Hossam M. Zawbaa, Ahmed Serag
Summary: This study introduces the grey wolf optimization (GWO) and antlion optimization (ALO) algorithms as variable selection tools in spectroscopic data analysis for the first time, showing that they select fewer variables than genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in most cases while maintaining almost the same performance.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Computer Science, Artificial Intelligence
Xiaobo Li, Qiyong Fu, Qi Li, Weiping Ding, Feilong Lin, Zhonglong Zheng
Summary: Feature selection is a multi-objective problem that aims to choose a subset of features with minimal feature-feature correlation and maximum feature-class correlation. Grey wolf optimization mimics the hunting mechanism of grey wolves but can face local optimization in multi-objective problems. To address this, a novel multi-objective binary grey wolf optimization algorithm called MOBGWO-GMS is proposed, which utilizes a guided mutation strategy (GMS). Experimental results show that the proposed approach outperforms other algorithms in terms of the optimal trade-off between fitness evaluation criteria and the ability to escape local optima.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Tuan Minh Le, Ly Van Tran, Son Vu Truong Dao
Summary: This paper focuses on the technology of fall detection systems for elderly people, proposes a novel feature subset selection method, and conducts classification experiments using various machine learning classifiers, showing that this approach is highly effective.
Article
Computer Science, Information Systems
Pradip Dhal, Chandrashekhar Azad
Summary: To filter spam emails, this research proposes a hybrid binary metaheuristic algorithm based on bat algorithm and grey wolf optimization for feature selection. The method achieves high classification accuracy and reduces the number of features for misclassifying legitimate emails as spam.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics
Farah Mohammad, Saad Al Ahmadi
Summary: This study explores the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of Alzheimer's disease (AD) prediction using the VGG19 deep learning model. By fusing features extracted from the fc7 and fc8 layers of VGG19, a comprehensive feature map is generated. Multiple machine learning algorithms are used to classify integrated features and recognize AD. The amalgamated feature map achieves a significant accuracy rate of 98% in AD prognostication, outperforming current cutting-edge methodologies.
Article
Computer Science, Information Systems
P. B. Pankajavalli, G. S. Karthick, R. Sakthivel
Summary: Today's sedentary lifestyle has led to an increase in lifestyle-related illnesses, prompting the search for ways to predict diseases before they manifest. Recent studies have focused on non-invasive methods utilizing wearable devices to predict stress, as traditional laboratory-based methods have not provided accurate results due to the subjective nature of stress patterns. Person-dependent models may offer higher accuracy, but require longer training periods. The proposed ANFIS-FWGWO classification algorithm shows promising results in accurately predicting stress levels by utilizing sensor data from a keyboard.
Article
Computer Science, Information Systems
N. Sivaselvan, K. Vivekananda Bhat, Muttukrishnan Rajarajan, Ashok Kumar Das, Joel J. P. C. Rodrigues
Summary: With the widespread use of IoT, security requirements have become an integral part. Authentication and access control are crucial for ensuring authorized and restricted access to resources. However, current approaches have limitations. Therefore, this paper proposes a capability-based unified authentication and access control system (SUACC-IoT) that utilizes lightweight cryptographic primitives. Experimental results show low computational and communication overheads.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Nilesh Kumar Jadav, Tejal Rathod, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Ahmed Alkhayyat
Summary: →Massive population growth and rising environmental issues pose challenges in agriculture, such as land scarcity, pesticide overuse, and global food demand. To tackle this, we proposed a blockchain and AI-powered smart agriculture framework to predict pesticide levels in crops. The blockchain ensures data integrity, storing records securely. Evaluation metrics show that our framework outperforms baseline approaches in accuracy, scalability, and latency.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Ashwin Verma, Pronaya Bhattacharya, Deepti Saraswat, Sudeep Tanwar, Neeraj Kumar, Ravi Sharma
Summary: Recently, UAVs have been used for COVID-19 vaccine distribution to address fake vaccine issues. The authors propose a blockchain-assisted UAV vaccine distribution scheme based on sixth-generation enhanced ultra-reliable low latency communication (6G-eRLLC). The scheme utilizes a public Solana blockchain setup for user registration, vaccine request, and distribution, ensuring scalable transactions. With an intelligent edge offloading scheme, UAV swarms are deployed to deliver vaccines to nodal centers, showing significant improvements in service latency, energy reduction, UAV coverage, and storage cost compared to 5G uRLLC communication and Ethereum network.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Junchao Yang, Feng Lin, Chinmay Chakraborty, Keping Yu, Zhiwei Guo, Anh-Tu Nguyen, Joel J. P. C. Rodrigues
Summary: Real-time digital twin technology enhances traffic safety and provides scientific strategies for intelligent traffic management. A parallel intelligence-driven resource scheduling scheme is proposed to address the delay and load balance issues in intelligent vehicular systems with dual dependencies of timing and data. An adaptive particle swarm with genetic algorithm is used to optimize the offloading, resource allocation, and load balance.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Information Systems
Shubhuam Singh, Pawan Singh, Sudeep Tanwar
Summary: Cloud computing is an innovative platform for consumer and business opportunities, allowing users to access apps and data from anywhere. It offers cost reductions for companies by borrowing storage and operational tasks from the cloud with a pay-as-you-go model. However, resource allocation is a crucial challenge in cloud computing. This paper proposes a new optimization-assisted approach for resource allocation that maximizes sales while minimizing costs.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Information Systems
Siva Sai, Vinay Chamola, Kim-Kwang Raymond Choo, Biplab Sikdar, Joel J. P. C. Rodrigues
Summary: Blockchain and AI technologies have independent applications in various industries and can be seamlessly integrated. AI algorithms can optimize the efficiency of medical blockchain storage and serve as knowledgeable gatekeepers. Blockchain can provide secure and diverse healthcare data for AI training. The integration of BC and AI has numerous use cases in healthcare, from disease prediction to pandemic management.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Mathematics
Pronaya Bhattacharya, Deepti Saraswat, Darshan Savaliya, Sakshi Sanghavi, Ashwin Verma, Vatsal Sakariya, Sudeep Tanwar, Ravi Sharma, Maria Simona Raboaca, Daniela Lucia Manea
Summary: The Metaverse enables the integration of physical and digital entities, promoting communication, transactions, and social interaction. With the advancement of Extended Reality technologies, the Metaverse is expected to revolutionize various industries by seamlessly blending the physical and virtual worlds. Assisted by emerging technologies such as 5G, blockchain, Web 3.0, AI, and NFTs, the Metaverse holds great potential in areas such as digital twins, telehealthcare, connected vehicles, virtual education, social networks, and finance.
Article
Mathematics
Aparna Kumari, Riya Kakkar, Rajesh Gupta, Smita Agrawal, Sudeep Tanwar, Fayez Alqahtani, Amr Tolba, Maria Simona Raboaca, Daniela Lucia Manea
Summary: In this paper, a real-time and secure incentive-based energy management system (EMS) for the smart grid is proposed using reinforcement learning and blockchain technology. The proposed approach introduces a novel reward mechanism and real-time incentive mechanism to encourage end-consumer participation and reduce energy consumption in peak hours, thereby increasing customer profitabilities.
Article
Telecommunications
Zhiwei Guo, Keping Yu, Kostromitin Konstantin, Shahid Mumtaz, Wei Wei, Peng Shi, Joel J. P. C. Rodrigues
Summary: With the development of green wireless communication, the green Internet of Vehicles (GIoV) has emerged as a potential solution for future transportation. Intelligent traffic forecasting for key nodes in GIoV is a significant research topic. This work combines deep embedding and graph embedding to propose a deep collaborative intelligence-driven traffic forecasting model in GIoV, aiming to establish more reliable feature spaces and improve forecasting efficiency. Experimental results on a real-world dataset show a reduction in forecasting deviation by about 15%-25%.
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
(2023)
Article
Green & Sustainable Science & Technology
Sanjoy Choudhury, Ashish Kumar Luhach, Joel J. P. C. Rodrigues, Mohammed AL-Numay, Uttam Ghosh, Diptendu Sinha Roy
Summary: Energy efficient ICT infrastructure is crucial for sustainable development in smart cities. This research tackles the virtual machine (VM) placement problem using a genetic algorithm (GA) to optimize data center efficiency and reduce energy usage and maintenance time.
Article
Agronomy
Devangi Hitenkumar Patel, Kamya Premal Shah, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Bogdan Constantin Neagu, Simo Attila, Fayez Alqahtani, Amr Tolba
Summary: Soil is crucial for agricultural produce quality and yield. This research proposes a crop recommendation algorithm based on soil attributes, which utilizes real-time data collected by soil sensors and validates the data using blockchain technology. The results are displayed on a user dashboard, allowing farmers to monitor their farm practices and sensor status remotely.
Article
Computer Science, Information Systems
Harshil Sanghvi, Rushir Bhavsar, Vini Hundlani, Lata Gohil, Tarjni Vyas, Anuja Nair, Shivani Desai, Nilesh Kumar Jadav, Sudeep Tanwar, Ravi Sharma, Nagendar Yamsani
Summary: The emergence of Web 3.0, blockchain technology, and artificial intelligence is transforming multiplayer online gaming in the metaverse. This article proposes a novel framework called MetaHate that uses AI and blockchain to detect and combat hate speech in online gaming environments. The research shows that combining machine learning models with gradient boosting achieves high accuracy. Additionally, using smart contracts to support hate speech moderation is an effective approach. The integration of AI and blockchain can significantly enhance the safety and inclusivity of the metaverse, highlighting the importance of these technologies in addressing hate speech and increasing user engagement.
SECURITY AND PRIVACY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Anand Nayyar
Summary: This paper proposes a blockchain and artificial intelligence (AI)-assisted onion routing framework to enhance the security and anonymity of military vehicles participating in the internet of military vehicles (IoMVs). By amending the AI algorithms to classify malicious and non-malicious IoMVs, and using blockchain for authentication and validation, this framework ensures that only non-malicious vehicles can exchange data through the onion routing network.
PROCEEDINGS OF 2023 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION TECHNOLOGY, ICIIT 2023
(2023)
Article
Computer Science, Information Systems
Kaushal Shah, Sarth Kanani, Shivam Patel, Manan Devani, Sudeep Tanwar, Amit Verma, Ravi Sharma
Summary: This article presents a blockchain-based object detection scheme using federated learning and distributed InterPlanetary File System. It eliminates the central authority and addresses the issues in traditional federated learning, achieving promising accuracy in various detection scenarios.
SECURITY AND PRIVACY
(2023)
Article
Computer Science, Information Systems
Pimal Khanpara, Kruti Lavingia, Rajvi Trivedi, Sudeep Tanwar, Amit Verma, Ravi Sharma
Summary: This paper discusses the security requirements of IoT-based smart homes as an essential subsystem of smart city architecture, and proposes a context-aware security-based scheme to prevent and detect possible security threats. Results show that the proposed scheme is superior to traditional schemes in terms of performance, cost, and maintenance requirements.
SECURITY AND PRIVACY
(2023)
Article
Computer Science, Hardware & Architecture
Jia Ke, Ying Wang, Mingyue Fan, Xiaojun Chen, Wenlong Zhang, Jianping Gou
Summary: This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Shi Qiu, Huping Ye, Xiaohan Liao, Benyue Zhang, Miao Zhang, Zimu Zeng
Summary: This paper proposes a multilevel-based algorithm for hyperspectral image interpretation, which achieves semantic segmentation through multidimensional information fusion, and introduces a context interpretation module to improve detection performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jianteng Xu, Qingguo Bai, Zhiwen Li, Lili Zhao
Summary: This study constructs two optimization models for the omnichannel closed-loop supply chain by leveraging the combined power of leader-follower game and mean-variance theories. The focus is on analyzing the performance of manufacturers who distribute products through physical stores. The results show that the risk-averse attitude of the physical store has a positive impact on the overall system profitability, but if the introduced physical store belongs to another firm, total profit experiences a decline.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jiahao Xiong, Weihua Ou, Zhonghua Liu, Jianping Gou, Wenjun Xiao, Haitao Liu
Summary: This paper proposes a novel remote photoplethysmography framework, named GraphPhys, which utilizes graph neural network to extract physiological signals and introduces Average Relative GraphConv for the task of remote physiological signal measurement. Experimental results show that the methods based on GraphPhys significantly outperform the original methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Zhiyao Tong, Yiyi Hu, Chi Jiang, Yin Zhang
Summary: The rise of illicit activities involving blockchain digital currencies has become a growing concern. In order to prevent illegal activities, this study combines financial risk control with machine learning to identify and predict the risks of users with poor credit. Experimental results demonstrate high performance in user financial credit analysis.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
