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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Alomran, Hanadi Mohammad Alhazmi, Omar Hussain Taibah University College of Computer Science and Engineering Department of Computer Science Madinah Saudi Arabia

ISBN: (纸本)9798350348910

Artificial intelligence (AI) is not a new term;its beginnings go back to the beginnings of the fifth decade in the year 1955. Since the emergence of artificial intelligence, it has been in the process of continuous development and spread of its technologies, until it recently reached the most prominent application, which is the ChatGPT program. AI technology is one of the most important modern technologies that contribute to the development of many fields, raise the level of quality, improve productivity, and increase efficiency. Education is one of the most important areas affected by modern technologies, which moved education from the ordinary level to the level of creativity and innovation. The idea of this research comes from several reasons: 1- The rapid transfer of education to a larger world in a short period, 2- The many positives that artificial intelligence had and acquired;3- The need for the teacher to develop himself in this field and keep pace with the modern era, 4- The absence of motivation among many students, and the infiltration of boredom and lethargy among their ranks. 5- The need for the courses taught by the student to adapt technology in this era, and the inclusion of artificial intelligence completes that. 6- Enriching the research library with a study related to the important topic needed by the teacher and the student. 7- The limited thinking of some students and the lack of scientific communication and its stopping at a certain point. © 2023 IEEE.

关键词： Artificial intelligence

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An Innovation Development of New Perspective of Efficient Approaches, Techniques and Challenges for Data Centers 2

An Innovation Development of New Perspective of Efficient Ap...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Shetty, Sahana Shashikala, H.K. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Data centers are essential components of the modern digital world. They provide the necessary resources and infrastructure to enable access to online services and applications, enabling individuals and organizations to communicate, collaborate, and access information in real time. Data centers are an essential part of the internet and are used to manage and store data, providing access to a wide range of services. They provide organizations with the ability to store, access, and process data. Data centers are the backbone of the digital revolution, enabling organizations and individuals to connect with the world. Data centers are essential for providing secure access to data and resources. They are designed with security features such as firewalls, encryption, and authentication that protect data from unauthorized access. Data centers also provide advanced networking and storage solutions to ensure data is secure, reliable, and available for use. Data centers are also important for reducing operational costs. They provide the necessary resources and infrastructure to reduce hardware and software costs, as well as energy costs associated with running hardware and applications. This reduces the overall cost of doing business and allows organizations to focus on their core competencies. © 2023 IEEE.

关键词： Cryptography

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

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BumbleBee: The Multi-purpose Voice Assistant 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Chakraborty, Devottam Pandey, Devansh Verma, Rajat Dhanda, Namrata Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Voice Assistants software agents to comprehend spoken language and respond with synthesized voices, voice assistants have revolutionized human-computer interaction. An application programming interface (API) called Bumblebee enables frictionless communication between users and Windows operating systems (OS). By eliminating the need for manual input using conventional input devices like keyboards or touchscreens, this voice control integration with Windows OS enhances user productivity and convenience. Users may easily control their environment, get information quickly, and streamline their daily activities by taking into account Bumblebee’s capability. In the end, Bumblebee is a strong API that enables users to interact with their Windows PCs via voice commands. By utilizing the power of natural language, it makes simpler to complete tasks like managing emails, controlling devices, organizing tasks, and executing requests. Voice control’s seamless integration with Windows OS completely changes how people interact with their computers, making it more simple, efficient, and user-friendly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Application programming interfaces (API)

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Adversarial Robustness in DeepFake Detection: Enhancing Model Resilience with Defensive Strategies 4

Adversarial Robustness in DeepFake Detection: Enhancing Mode...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Pallavi, N. Pallavi, T.P. Bylaiah, Sushma Goutam, R. M S Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka India Karnataka India Atria Institute of Technology Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331506490

Detection of DeepFakes presents formidable challenges primarily due to adversarial attacks that can dramatically reduce model accuracy. Current detection models are reasonably effective in real scenarios;however, these detectors are vulnerable to adversarial attacks, resulting in misclassifications. The current study examines defensive approaches to strengthen the robustness of DeepFake detection approaches when facing adversarial attacks. Explored several alternatives, including adversarial training, input preprocessing, and model ensembles against a manipulated media dataset. The results demonstrate that the model has predicted image appropriately at average of 86.34% but after the adversarial attacks the prediction of the image was 99.8% inappropriate. although adversarial attacks greatly lower the effectiveness of a model, it is possible to improve the robustness of a model through defense methods, particularly adversarial training in combination with an ensemble method which predicted the image appropriately at an average of 52.06%. However, despite the improvement, the defense methods still fail to protect all adversarial examples. The results illustrate the importance of developing new methods of defense against DeepFake detection in light of future adjustments and variants of an attack. © 2024 IEEE.

关键词： adversarial attack attacks deepfake deeplearning detection system

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Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

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China Communications 2022年第10期19卷 12-20页

作者： Yunus Dursun Fang Fang Zhiguo Ding School of Electrical and Electronic Engineering University of ManchesterManchesterUK Department of Electrical and Computer Engineering and the Department of Computer Science Western UniversityLondonCanada

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

关键词： NOMA MEC MIMO Generalized sin-gular value decomposition sixth generation networks(6G) delay minimization

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing 11

Comparative Analysis of Edge Detection Algorithms with Parti...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahayu, Sri Nurtantio Andono, Pulung Pujiono Zainul Fanani, Ahmad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia Institut Teknologi Garut Computer Science Department Garut Indonesia

ISBN: (纸本)9798350389654

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition 3rd

Deep Learning for Cognitive Task and Seizure Classification ...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Variational mode decomposition

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The Smart Development of Human Thinking Prediction Using Complex Fuzzy Systems 2

The Smart Development of Human Thinking Prediction Using Com...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kanniga, Devi Arvind, K.S. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The use of complex fuzzy systems to predict human thinking is an area of active research. These systems are based on fuzzy logic, which is an approach to computing based on approximation and imprecision. Fuzzy logic has been used in a variety of areas, such as control systems, image processing, decision support systems, and even robotics. The idea behind fuzzy logic is to use a combination of fuzzy rules, fuzzy sets, and fuzzy inference to approximate the decisions that humans make in complex situations. This means that the system can take into account the uncertainty of the situation and make a decision based on available data. The system can also be trained to recognize patterns in the data and make predictions about future decisions. In order to predict human thinking, a complex fuzzy system needs to be able to take into account a variety of factors, such as situational context, emotions, and values. For example, if a person is deciding whether to buy a car, the system would need to consider factors such as price, reliability, and environmental impact. The system would also need to consider the person's preferences, such as their preferred color or style. © 2023 IEEE.

关键词： Control systems

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A Review on Transformer Models: Applications, Taxonomies, Open Issues and Challenges 4

A Review on Transformer Models: Applications, Taxonomies, Op...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Passi, Nidhi Raj, Manish Shelke, Nitin Arvind Bennett University School of Computer Science and Engineering Greater Noida India Keshav Mahavidyalaya University of Delhi Department of Computer Science India

ISBN: (纸本)9798350354218

With each passing generation, the human language is getting rich vocabulary usually effected by regional, domain specific and emotional dialects. Natural Language Processing (NLP) deals with training machines the human context of verbal or textual expressions. Over the years transformers have played a crucial role in this regard with substantial advancement. Sequence to sequence (Seq2Seq) models advancements with the incorporation of attention mechanism helped in grasping relationship between different sequences for better understanding. The shortcomings of the model further surfaced giving way to amendments like pretraining on extensive datasets and using varied attention mechanism bringing into forefront of research Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) models. Architectures based on Transformers have ventured strongly into the realm of generative modelling by classifying and understanding images-based data with models such as BERTvision, DALL-E and CLIP. Our analysis discusses the trajectory of Transformers. Current research endeavours emphasize on innovative designs, increased effectiveness, and expanded generalization capabilities. The discovery of Transformers represents a significant change in machine learning, since it enables models to understand intricate data across many modalities, hence improving the capabilities of artificial intelligence. © 2024 IEEE.

关键词： Distribution transformers

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