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International Conference on Computing and Networking Technology (ICCNT)

作者： Sucheta Chandra Priyanka Das Dept. of Computer Application and Science Institute of Engineering & Management Kolkata

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Sybil attacks pose a significant threat to the security and integrity of online social networks by allowing malicious actors to create multiple fake identities to manipulate network behavior. The abstract outlines a method for detecting Sybil attacks in online social networks using graph analysis. It involves constructing a graph representation of the network, calculating node centrality measures such as in-degree and out-degree, and setting a degree threshold to identify potentially malicious nodes. Additional parameters like clustering coefficient and community structure are also considered to improve detection accuracy. The proposed approach aims to enhance the security of online social networks by identifying and isolating Sybil identities, thus preserving network integrity and trustworthiness. The authors’ firmly assert that employing graph-based theory for Sybil node detection in online social networks stands out as one of the most effective methods. This approach not only offers robustness but also scalability, allowing for efficient detection even in large-scale networks. With its ability to unveil intricate relationships, anomalies, and patterns within the network, graph-based theory emerges as a cornerstone in the fight against Sybil attacks.

关键词： Accuracy Social networking (online) Scalability Robustness Security

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Water Quality Prediction using Classification techniques on XGBoost, KNeighbors, SVC, Random Forest, AdaBoost, and GaussianNB Classifier

Water Quality Prediction using Classification techniques on ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gill, Kanwarpartap Singh Tuteja, Gaurav Anand, Vatsala Gupta, Rupesh Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9798350308167

A key environmental problem that is extensively researched for academic purposes is water contamination. To understand the origins, impacts, and potential solutions to water contamination, researchers look into a variety of issues. Environmental science, chemistry, biology, engineering, and policy are essential for study of water pollution. Classifiers as proposed help create early warning systems for probable pollution events by categorising water quality based on previous and current data. These systems aid in prompt reactions to occurrences of pollution prevention or mitigation. It is essential to tackling one of the most urgent issues affecting the wellbeing of ecosystems and human communities. By training models to predict the degree of pollution or the category of pollutants based on input characteristics, machine learning classifiers can classify water contamination. This study focuses on XGBoost, KNeighbour, SVC, Random Forest, Adaboost, and GaussianNB classifiers' various classification methodologies for predicting water quality. The XGBoost classifier, which may be used for future study, emerges as the best classifier out of all of these. © 2023 IEEE.

关键词： Artificial Intelligence Classification Data science Deep Learning Machine Learning ROC Curve Water Pollution

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Adchat-TVQA: Innovative Application of LLMs-Based Text-Visual Question Answering Method in Advertising Legal Compliance Review

Adchat-TVQA: Innovative Application of LLMs-Based Text-Visua...

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Machine Learning and computer Application (ICMLCA), International Conference on

作者： Ruiling Gao Yingqi Hou Weijie Tang Jin Zhang Yanbo Hu Haiyun Huang Wen'an Tan Liping Li Dept. of Computer Engineering Shanghai Polytechnic University Shanghai China Dept. of Computer Science Donghua University Shanghai China Dept. of Computer Science Tufts University Medford MA USA

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Advertising legal compliance reviews have always been time-consuming and labor-intensive, and existing Large Language Models(LLMs) are far worse performing than senior industry experts. In this paper, we propose a novel LLMs-based text-visual question answering method called Adchat-TVQA for advertising legal compliance review. After reading an advertising image and the corresponding review question, it will first understand the content on the image through multimodal learning, then optimize the question with Zero-Shot Chain-of-Thought (CoT) prompting method based on predefined industry expert experience, and finally input the augmented question into the large language model for inquiry. The feasibility of this method is verified through system prototype implementation and end-to-end functional tests. In addition, we summarized the characteristics of advertising images and added image segmentation and text box transpose to the data processing process for system performance improvement. Performance testing of the text visual cognitive question task on the AIWIN2021 dataset shows that the method scores higher than Microsoft's LayoutLM, with an increasement of the score from 51.52% to 52.74%.

关键词： Industries Visualization Reviews Law System performance Prototypes Question answering (information retrieval) Prompt engineering Advertising Testing

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Sentiment Analysis of Customer Reviews on Food Ordering Portals of Bangladesh using Natural Language Processing

Sentiment Analysis of Customer Reviews on Food Ordering Port...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Priom Deb Asibur Rahman Bhuiyan Farhan Ahmed Md. Rakib Hossain Habiba Mahrin Md Faisal Ahmed Dewan Ziaul Karim Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

In recent years, the popularity of online food ordering services has surged, offering consumers a convenient way to order food from restaurants and have it delivered to their doorstep. During this period, HungryNaki and Foodpanda Bangladesh have been identified as key contributors to the growth and advancement of the online food delivery market. This study aims to anticipate the sentiments of Bangladeshi customers towards online meal ordering services, with a specific focus on Foodpanda Bangladesh and HungryNaki. We created a new dataset for this research, subjected it to preprocessing and employed six machine learning models and three deep neural network models. In the machine learning approach, the Random Forest Classifier demonstrated excellence in accuracy, precision, and recall, achieving an accuracy rate of 80.31%. On the other hand, the BERT Classifier performed effectively in the deep learning approach, reaching a peak accuracy of 89%. Despite challenges such as data ambiguity and imbalances in the dataset, our findings underscore the potential of the BERT model in sentiment analysis, offering valuable insights for future research in this field.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Reviews Artificial neural networks Manuals Random forests Portals Optimization

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Leveraging Virtual Reality and AI Tutoring for Language Learning: A Case Study of a Virtual Campus Environment with OpenAI GPT Integration with Unity 3D

arXiv

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arXiv 2024年

作者： Adithya, T.G. Abhinavaram, N. Srinivasa, Gowri Dept. Computer Science & Engineering PES University Bangalore India

This paper presents a new approach to multiple language learning, with Hindi the language to be learn’t in our case, by using the integration of virtual reality environments and AI enabled tutoring systems using OpenAI’s GPT api calls. We have developed a scenario which has a virtual campus environment using Unity which focuses on a detailed representation of our university’s building's 11th floor, where most of the cultural and technological activities take place. Within this virtual environment that we have created, we have an AI tutor powered by OpenAI's GPT model which was called using an api which moves around with the user. This provided language learning support in Hindi, as GPT is able to take care of language translation. Our approach mainly involves utilising speech to text, text to text conversion and text to speech capabilities to facilitate real time interaction between users and the AI tutor in the presence of internet. This research demonstrates the use of combining VR technology with AI tutoring for immersive language learning experiences and provides interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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A Study on Deep Fake Face Detection Techniques

A Study on Deep Fake Face Detection Techniques

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Mudit Arya Priyanshu Upwan Akash Umang Goyal Simran Chawla Dept. of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era.

关键词： Deepfakes Ethics Reviews Explainable AI Neural networks Privacy breach Trajectory

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Personality Analysis Using Graphology and Image Processing Techniques

Personality Analysis Using Graphology and Image Processing T...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Anshika Jadon Harsh Gutpa Er. Hatesh Shyan Aayush Tyagi Tejaswi Raj Dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This research studies the role of advanced image processing and graphology at a higher level of complexity, that is, predicting grief by interpreting handwriting. The aim of this study is to establish a reliable model, which gets a connection between the deep learning techniques and a certain set of handwriting parameters that would depict a person's mental state. This method combines the foundations of traditional graphology with the newest advancements in computer technology, such as TensorFlow and Keras, and use CNNs for feature extraction. Advanced machine learning methods including PCA, Random Forest, SVMs, and Gradient Boosting are used to improve the model's ability to classify emotional states from handwriting. The results show that the model has established discriminative capability in classification of 85% of samples with precision and recall values at 88% and 82%, respectively. This research amalgamating the traditional graphology with modern practices in psychology provides an opportunity for further enhancements in mental health evaluation through handwriting-based approaches.

关键词： Deep learning Sentiment analysis Computational modeling Mental health Feature extraction Depression Complexity theory Reliability Random forests Principal component analysis

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Proposal for a Virtual Reality System to Practice Knife Handling in Cooking

Proposal for a Virtual Reality System to Practice Knife Hand...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yukitoshi Nakazato Toru Kano Dept. Computer Science and Engineering Ibaraki Univercity Ibaraki Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Knife handling is one of the most important skills in cooking. However, it is difficult to practice the handling of a kitchen knife repeatedly owing to limitations in resources. To address this, we propose a highly realistic system using a haptic device and CT (Computed Tomography) data. This system allows users to practice knife handling in a virtual environment, providing force feedback that reproduces real-life situations. Our goal is to enhance the efficiency and effectiveness of cooking training by enabling unlimited practice without the constraints of ingredients.

关键词： Training Computed tomography Force feedback Virtual environments Proposals Consumer electronics

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Automated Waste Management: A Deep Learning and Cloud Based Food & Plastic Waste Separator Bin

Automated Waste Management: A Deep Learning and Cloud Based ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Shaharia Hossen Ahmed Jebail Shoukhin Nazmul Hasan Athin Anindita Chakma Md. Motaharul Islam Dept. of Computer Science and Engineering United International University Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Waste management techniques should be improved to reduce environmental pollution. Problematic situations arise while plastic pollution occurs. The challenging task is to collect plastic waste from the environment. In this era, computer vision helps solving real-life problems. Environmental problems can be sorted out using the advanced technology associated with computer vision. Using deep learning models, different types of food waste and plastic can be identified. In this research work, an automated waste separator bin is implemented using deep learning models and cloud computing. After going through the experimental process with different models, the YOLOv8 object detection model achieves the best mean average precision (mAP50) of 99%. The automated waste separator bin has a multi-box system for collecting categorical waste. Using this intelligent system, some recyclable waste, like plastic, can be sorted out and reduced from mixing with the food waste and other categorical waste. Waste management will be improved by the ability to separate plastic and food waste with this automatic bin at the initial stage of waste collection.

关键词： Waste management Food waste Deep learning computer vision Cloud computing Pollution Particle separators Computational modeling Usability Plastic waste

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Hybrid Image Encryption for IoT Applications: Integrating Cellular Automata and Henon Map to Improve Security and Performance

Hybrid Image Encryption for IoT Applications: Integrating Ce...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Biswarup Yogi Ajoy Kumar Khan Satyabrata Roy Dept. of Computer Engineering Mizoram University Aizawl Mizoram India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Securing the vast volumes of data exchanged over networks is crucial in IoT (Internet of Things) applications. This study introduces an innovative image encryption algorithm that combines the interactive properties of Cellular Automata (CA) and the Henon map to improve both security and performance. CA, renowned for its simplicity and parallelism, are utilized to produce intricate pseudorandom sequences. These sequences are then used as the keystream for encryption purposes. Whereas, the Henon map is employed to incorporate further levels of unpredictability and intricacy into the encryption procedure. The proposed method is thoroughly assessed through considerable experimentation and is compared with existing methodologies. The findings demonstrate substantial enhancements with respect to various standard metrics such as “Mean Squared Error (MSE)”., “Peak Signal-to-Noise Ratio (PSNR)”., “Unified Average Changing Intensity (UACI)”., and “Number of Pixels Change Rate (NPCR)” measurements., underscoring the resilience and efficacy of the proposed methodology. The results emphasize the capability of this hybrid method to offer a secure and robust performance for image encryption in IoT applications. This method guarantees the integrity and confidentiality of data while outperforming current encryption techniques in performance.

关键词： Image quality PSNR Measurement uncertainty Automata Parallel processing Encryption Internet of Things Standards Resilience Random sequences

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