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The Social Score: A Social Credit System Using Blockchain 5

The Social Score: A Social Credit System Using Blockchain

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ubale, Ganesh Bawake, Vasundhara Badkas, Kshitij Bhade, Shourya Batkadli, Aarya Belure, Prasad Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331518097

The Chinese Social Credit System (SCS) serves as a large-scale example of behaviour tracking, adapted here for a college-level social scoring framework. The system monitors student activities, achievements, and certificates to promote positive behaviour. Certificates generated by administrators are hashed and stored on the blockchain, creating an immutable, tamper-proof record that cannot be altered or forged. Blockchain technology facilitates decentralized verification, allowing certificates to be authenticated independently when uploaded by students to enhance their social scores. This approach fosters trust through enhanced transparency and security. The student dashboard provides access to certificate uploads, activity records, violation reporting, and reward monitoring, while the administrative interface supports the management of reports, violations, leaderboards, and certificate generation. The blockchain-based design ensures that processes, from certificate issuance to verification, remain secure, transparent, and resistant to tampering, establishing a reliable framework for managing social scores. © 2024 IEEE.

关键词： Blockchain

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Research on the Influence of Artificial Intelligence Generated Content (AIGC) on Virtual Community Marketing 4

Research on the Influence of Artificial Intelligence Generat...

引用

4th IEEE International Conference on Information technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Ziyi Yuan, Xinwei Xi'an University of Technology Institute of Data Science and Decision Intelligence Xi'an China

ISBN: (纸本)9798350363586

As an important branch of the field of artificial intelligence, the technology of Artificial Intelligence Generated Content (AIGC) has developed rapidly in recent years. With the popularity of the Internet and the development of social media, virtual community marketing has become one of the important means of enterprise marketing. More and more enterprises begin to try to apply AIGC technology to virtual community marketing, which has brought new opportunities and challenges for virtual community marketing. This study builds a conceptual model to explain the influence of AIGC on virtual community marketing. This model is tested on the survey data from some virtual communities in China. The empirical results show that the influence of AIGC (content multimodality and content interactivity) on virtual community marketing (forwarding intention and brand attitudes) are mediated by users' perceived interestingness and perceived usefulness. © 2024 IEEE.

关键词： Virtual environments

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CogniVisio: Fusion Image Classifier 2

CogniVisio: Fusion Image Classifier

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mundargi, Zarinabegam Shinde, Aryan Bahirat, Shravani Das, Shreya Deokar, Om Shetiya, Dipasha Vishwakarma Institute of Technology BTech- Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350360523

CogniVisio is an innovative project that leverages state-of-the-art technologies such as Google Colab, Python, TensorFlow, Convolutional Neural Network (CNN) Inception for developing a robust image classification system. The project addresses the challenge of enhancing image classification accuracy by employing a fusion approach that combines the strengths of image generation and the powerful feature extraction capabilities of CNN Inception. With a dataset comprising 8732 images, our methodology achieves an impressive accuracy of 86.86%, showcasing the effectiveness of the proposed fusion image classifier. The utilization of cloud-based computing through Google Colab provides scalability and accessibility, making CogniVisio a versatile solution for image classification task. © 2024 IEEE.

关键词： Image recognition

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Reinforcement Learning for Fuel Efficient Driving on Hilly Terrain - Maximizing Horizontal Distance Travelled 15

Reinforcement Learning for Fuel Efficient Driving on Hilly T...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anudheer, Parimi Padakanti, Ved Krishna Mohamed, Sheena Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798331300579

Reinforcement learning is a crucial technical achievement in the present era, playing a key role in generating substantial progress in artificial intelligence and machine learning applications across several areas. The application of this dynamic methodology empowers machines and software agents to independently acquire knowledge and enhance their performance through their interactions with their surroundings, hence improving problem-solving and decision-making activities. This study delves into the application of reinforcement learning techniques to train an autonomous agent within a customized Mountain Car climbing environment. The primary objective revolves around optimizing fuel usage to propel the car uphill, strategically balancing the need for ascent against the subsequent horizontal distance covered during the descent. The constrained resource, finite fuel, necessitates the agent's adaptation through a training process to discern an optimal fuel consumption strategy. The research underscores the iterative nature of this training process, wherein the agent learns to navigate the trade-off between fuel utilization and distance traveled. In essence, this investigation contributes to the realm of reinforcement learning by elucidating the agent's capacity to make informed decisions. © Grenze Scientific Society, 2024.

关键词： Reinforcement learning

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Machine Learning Approaches for Underwater Object Classification using Sonar data 1

Machine Learning Approaches for Underwater Object Classifica...

引用

1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Sowmya, B.J. Mugada, Swathi Swetha, B.N. Vijaya Kumar, B.P. Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350376517

The need for analyzing a sonar dataset using machine learning algorithms arises from critical applications such as naval operations, marine exploration, and environmental monitoring. Accurate classification of underwater objects, including rocks and mines, is essential for maritime safety, environmental assessment, and underwater security. Manual interpretation of sonar data is time- consuming and error-prone, necessitating automated classification methods. By leveraging machine learning, we aim to develop robust models capable of efficiently identifying underwater objects, thereby advancing underwater sensing technology and supporting various marine-related endeavors. Here, we investigate the efficacy of Random Forest and Linear Regression machine learning algorithms in analyzing a sonar dataset comprising frequencies to predict whether an object is a rock or a mine. The primary objective is to compare the performance of these two algorithms in classifying underwater objects based on acoustic signals. By leveraging machine learning techniques, we aim to develop models capable of effectively discriminating between rocks and mines based on their acoustic signatures, thus addressing the challenge of underwater object identification. Therefore, the experimental setup involves preprocessing techniques to enhance feature extraction and mitigate noise, ensuring the robustness of the model and optimizing computational efficiency without compromising accuracy. © 2024 IEEE.

关键词： Random forests

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Design and Evaluation of an AI-Powered Conversational Agent for Personalized Mental Health Support and Intervention (MindBot) 5

Design and Evaluation of an AI-Powered Conversational Agent ...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kambare, Shweta M. Jain, Kriya Kale, Ishika Kumbhare, Vibhor Lohote, Sakshi Lonare, Shubham Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331530013

This research study describes the design and evaluation of an interactive MindBot to help a person deal with depressive Sentiments and to support individuals experiencing depressive symptoms by providing timely, interactive, and empathetic engagement. With the growing trend of mental health issues and a lack of adequate resources to cope, the need for innovative solutions is of great importance. In this MindBot, users will be connected to each other to communicate their Sentiments and seek support. The integration of NLP and LLMs by the chatbot fosters meaningful conversations and ensures timely support to users. Computational architecture optimizes response generation to control the processing cost related to LLMs while striking a balance between conversational richness and efficiency. This method allows for real-time communication without sacrificing the caliber of involvement. The chatbot's integration of NLP and LLMs promotes meaningful discussions and guarantees that consumers receive prompt service. The utilization of predefined response templates combined with dynamic responses generated by GPT -3.5-turbo further improved the implementation's performance in conversational capability. The paper covered the system architecture, methodology, and results achieved, with an emphasis on the contribution of sentiment analysis and the contributions of the chatbot towards mental health support. User happiness, emotional correctness, referral appropriateness, and the model's capacity to sustain coherent conversation even in the face of small user input errors are all used to gauge how effective the model is. The results indicate that the effectiveness of mental health interventions and user engagement are increased when sentiment analysis and sophisticated language models are combined. It was demonstrated that the impact of integrating advanced language models along with sentiment analysis can have a huge bearing on enhancing the user engagement factor as well as the ef

关键词： Chatbots

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Engage Learn: An AI-Based English Proficiency Improviser 4

Engage Learn: An AI-Based English Proficiency Improviser

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Jadhav, Balasaheb Jain, Manas Jajoo, Adhip Kadam, Devika Kadam, Harshvardhan Kakkad, Toshish Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331529635

In this research, the new and highly effective Web-based English learning and teaching platform called Engage Learn is introduced with the main purpose of avoiding the mistakes of the traditional models and to help users gain the ability to write and speak English more effectively. Engage Learn employs a marked-defined approach, that involves the application of several advanced technologies. Generative models including ChatGPT 3.5 turbo and Gemini 1.5 flash get enhanced presence when trained using Retrieval-Augmented Generation (RAG) technique. First, the ability for users to speak English makes for an almost informal procedure when learning since the user and the computer are communicating with one another by use of words. In addition to this, targeted feedback modules make an assessment of the user's input and give an immediate-feedback of what is required in terms of improving a user's vocabulary, grammar, and fluency. It offers another learning approach since it presents the content in a manner that is suited more to the current ability level of the user and the usage of preferred learning objectives. Engage Learn also provides flexibility of use based on learning modes as encompassed by general conversation practice, focused grammar correction and vocabulary buildup. This paper reviews the system architecture, explaining features of the different parts of the system, including the speech recognition, LLM with RAG, output analyzing, and the feedback-giving modules on grammar, vocabulary, and reading preferences. Further the paper discusses the tuning of the LLMs in a carefully selected set for language acquisition. This fine-tuning makes sure that the LLMs shall have adequate capability and knowledge required so as to get a feel about how spoken English is and about the particular feedbacks that different user may require out of them. In the last section of the abstract, the planned evaluation methods are presented with the focus on the responsibility of user te

关键词： Contrastive Learning

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Detecting and Responding to Cloud Privilege Escalation Attacks Using Machine Learning 6

Detecting and Responding to Cloud Privilege Escalation Attac...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Tabassum, Sheema Ramana, K. Chaitanya Bharathi Institute of Technology Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331505790

The field of cloud computing has created as an extremely productive region for complete innovative work, drawing in critical consideration from proficient organizations and organizations the same. The quick multiplication of PCs, information organizations, and other basic applications has brought about a more prominent accentuation on network safety. This exploration utilizes state of the art ML procedures to distinguish and forestall honor acceleration dangers, consequently supporting cloud security. Strong insurances are fundamental since utilizing cloud administrations builds the risk of these attacks. To work on generally speaking security, the review targets shortcomings in representative access freedoms in cloud settings utilizing ML methods like LightGBM, RF, AdaBoost, and XGBoost. This empowers ongoing discovery and relief of honor acceleration endeavors while adjusting quickly to evolving dangers. Moreover, a 'soft' Voting Classifier that joins forecasts from RF, DT, and SVM supports framework execution considerably further. The blend of a simple to-utilize Flask framework with SQLite gives secure information exchange and sign-in usefulness while enhancing client testing for true arrangement. This far reaching technique not just further develops client and business information security and encourages distributed computing trust, however it likewise helps cloud specialist co-ops' and associations' trust in their ability to keep a protected web-based climate. Among the proposed calculations, the Voting Classifier scored the best execution accuracy of 96.45 %, while LightGBM accomplished 94 %. The leftover accuracy scores are as per the following: RF (95 %), AdaBoost (95 %), and XGBoost (9 4 %). © 2024 IEEE.

关键词： Cloud security

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Automated Medicinal Plant Identification through Image Processing and Machine Learning 8

Automated Medicinal Plant Identification through Image Proce...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Kadam, Yash Yache, Ajay Solunke, Pavankumar Tati, Ramakant Kachhoria, Renu Buchade, Amar Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350391770

This research addresses the pressing challenge of accurately identifying medicinal plants in India's Ayurvedic Pharmaceutics, aiming to mitigate issues stemming from morphological variability and market adulteration. Traditional methods of plant identification are hindered by laborious processes and the potential for error, underscoring the need for an automated solution. Leveraging the power of advanced image processing and machine learning, our proposed software framework integrates the Xception model and early stopping fine-tuning to automate plant identification based on plant images. The methodology involves curating a comprehensive dataset of labelled plant images representing diverse medicinal plants, subsequently employing image preprocessing techniques to extract salient features like color, texture, and shape. The Xception model, renowned for its prowess in image recognition tasks, serves as the backbone architecture for both feature extraction and classification. To further optimize model performance and mitigate overfitting, early stopping fine-tuning techniques are applied. Through rigorous experimentation and validation, our research demonstrates the efficacy of this approach in accurately identifying medicinal plants, thereby fostering confidence and trust within the herbal medicine supply chain. By seamlessly integrating modern technological advancements with traditional botanical knowledge, our framework not only streamlines the identification process but also contributes to the sustainable utilization of herbal medicine resources. Ultimately, this research represents a pivotal step towards establishing a robust and reliable system for identifying medicinal plants, facilitating informed decision-making, and promoting the long-term viability of herbal medicine practices in India and beyond. © 2024 IEEE.

关键词： Medicinal chemistry

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