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Survey and Tutorial on Hybrid Human-Artificial Intelligence

Tsinghua science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

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computers, Materials & Continua 2024年第8期80卷 1809-1829页

作者： Yu Zhou Yun Zhang Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu North China Institute of Computing Technology Beijing Beijing100080China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

关键词： Component vehicular dynamic task offloading resource scheduling

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On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

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Neutrosophic Sets and Systems 2025年 76卷 1-17页

作者： Wazir, Samar Beg, M.M. Sufyan Ahmad, Tanvir Sohail, Shahab Saquib Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Computer Engineering Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India

The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA operator. The work is further expanded by proposing two additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA operator along with multiple correlation analysis for more than two items in uncertain transactional databases. The effectiveness of the proposed methods is evaluated by running the algorithms on both standard and example datasets, with results compared to the traditional probabilistic approach for identifying frequent and interesting items through multiple correlation. While multiple correlation analysis highlights the relationships of interestingness and uninterestingness between items, the OWA operator enhances the results when combined with fuzzy means and probabilistic methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, which is anticipated to open new avenues for further exploration in this field. © 2025, University of New Mexico. All rights reserved.

关键词： Database systems

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Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language

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International Journal of Speech Technology 2024年第3期27卷 687-700页

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Vekkot, Susmitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & amp Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2575-2588页

作者： Diao, Yufeng Zhang, Yichi She, Changyang Zhao, Philip Guodong Li, Emma Liying University of Glasgow School of Computing Science Glasgow United Kingdom University of Manchester Department of Computer Science Manchester United Kingdom University of Glasgow School of Engineering Glasgow United Kingdom University of Sydney School of Electrical and Information Engineering Sydney Australia

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates taskoriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution. © 1983-2012 IEEE.

关键词： Semantic Segmentation

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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering 4

Seamless Service Evolution: Enhancing Customer Satisfaction ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ali, Mohammed Jaffer Vyshnavi, M.R.V. Kumar, Konda V.S. Harshith Vishnubhatla, Sravya Rajagopal, Shinu M. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes. © 2024 IEEE.

关键词： Scalability

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Anomaly Detection in Classroom Using Convolutional Neural Networks 6th

Anomaly Detection in Classroom Using Convolutional Neural Ne...

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6th International Conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Vidhyasagar, B.S. Doppalapudi, Harshith Chowdary, Sritej Dagumati, VishnuVardhan Charan Kumar Reddy, N. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India

ISBN: (纸本)9789819984503

Anomaly detection is a task that involves identifying patterns or events that deviate significantly from the expected or normal behavior. In the context of classroom settings, it is essential to identify mischievous behavior exhibited by students to ensure a productive and conducive learning environment. This paper proposes a new method to identify abnormal behavior in a classroom setting using computer vision and deep learning techniques. This method uses convolutional neural networks (CNNs) and recurrent neural networks (RNN) algorithms to detect hand-raising, sleeping, talking, and fighting behaviors in the classroom with live video surveillance. It involves using the You Only Look Once (YOLO) algorithm to locate the position of students in the frame and an RNN algorithm to classify their behavior as normal or anomalous. RNN algorithm is trained on a labeled image and video dataset containing examples of normal and abnormal behaviors. This system can trigger real-time alerts, allowing teachers to act appropriately to maintain a conducive learning environment. It also helps to identify and address disruptive behaviors. Real-time, revolutionizing classroom monitoring: Our algorithm has the potential to improve student safety and well-being, as well as their academic performance. This research could lead to further developments in the field of the classroom. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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