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Revolutionizing Image Analysis for Precision Diagnosis and Recognition Using Cutting-Edge Visual Encoding

Revolutionizing Image Analysis for Precision Diagnosis and R...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Rajalakshmi, D. Anita, C.S. Sasikumar, R. Sudharson, K. Alekhya, Badi Rose, S. Remya R.M.D. Engineering College Department of Computer Science and Engineering Chennai India R.M.D. Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350348460

This groundbreaking study introduces a revolutionary image analysis method, showcasing unprecedented accuracy in precision diagnosis and recognition. The innovative approach seamlessly integrates feature detection and description, demonstrating exceptional proficiency in identifying crucial local features. The advanced visual encoding architecture, surpassing conventional methods, utilizes intricate convolutional layers and the renowned Haar algorithm, achieving a remarkable 99% success rate. This comprehensive suite of advancements propels image analysis to new heights, with potential applications ranging from medical diagnostics to facial recognition. Strongly accepted for its cutting-edge techniques and impressive success rate. © 2024 IEEE.

关键词： facial recognition feature detection image analysis precision diagnosis visual encoding

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Vijay, K. Sowmia, K.R. Ravikumar, S. Arockia Raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for an array of languages to meet the diverse expectations. Reinforcement learning retrieves user rating to provide better performance and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Assessing the Efficacy of machine learning and Deep learning in the Field of Cyber Security

Assessing the Efficacy of Machine Learning and Deep Learning...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Eswarawaka, Rajesh Nijim, Mais Kanumuri, Viswas Albetaineh, Hisham Acm Engineering College Department of Machine Learning and Computer Science Karnataka Bangalore560076 India Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350327595

The use of machine learning has become widespread across various fields because of its superior performance compared to conventional rule-based algorithms. As a result, these models have also been integrated into cyber security systems, machine learning is being utilized to aid or possibly even supplant the role of human security analysts. However, it's important to evaluate the effectiveness of machine learning in cyber security with careful consideration, especially if complete automation of detection and analysis is being considered. This study provides an in-depth research focuses on machine learning techniques applied in intrusion, malware, and spam detection that are tailored towards security professionals. The primary objective of our study is to evaluate the degree of advancement or maturity of these techniques of ML-based cybersecurity solutions and to identify any limitations that could impede their effectiveness as detection mechanisms. To achieve this, we conducted a thorough literature review and performed experiments on enterprise systems and network traffic in real-world settings. Our goal is to gain understanding of the capabilities and limitations of ML solutions and provide actionable insights for their improvement. © 2023 IEEE.

关键词： cyber security machine learning and deep learning

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Turnstile p leverage score sampling with applications 41

Turnstile p leverage score sampling with applications

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41st International Conference on machine learning, ICML 2024

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ Rn×d, proportional to their p norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to Ε relative error. When combined with preconditioning techniques, our algorithm extends to p leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective off-line subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + Ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/Ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for p and logistic regression. Copyright 2024 by the author(s)

关键词： Data streams

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Intelligent Management Information System: Web Approach 15

Intelligent Management Information System: Web Approach

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

作者： Sujatha, E. Devi, S. Renuka Subha, S. Sree Raju, D. Naveen Loganathan, V. Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Aims to develop web application for information management and sharing which helps educational institutions broadcast information to the students and their faculties. A college generates and uses a large amount of data where it needs to be stored in the centralized repository. This data must be communicated appropriately from the authorities to students, faculty, and HOD. This web application resolves to store, manage, and distribute this information. It is intended for information management and sharing in a way that can manage and manipulate data in a centralized place which helps us reduce the time required for updating, storing, sharing the information a college is generating. And eliminates duplication and maintains consistency in data and using one of the latest and powerful concepts called session which helps us store the user information across multiple web pages. The goal of the of web application for information management and sharing is to achieve centralized data management and accessibility. © Grenze Scientific Society, 2024.

关键词： Information hiding

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Water Management for IoT-Based Smart Agriculture Using machine learning Algorithms

Water Management for IoT-Based Smart Agriculture Using Machi...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Geetha Rani, E. Tukkoji, Chetana Anusha, D. Dhanalakshmi, M. Bharath, B. Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Gitam University Karnataka Bengaluru India Department of CSE- Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Canteen Stores Department MVJ College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture relies substantially on water. The IoT-based farm water management system explains how IoT technology can control irrigation intelligently. This system may be used to collect data by connecting several sensors such as soil moisture, temperature, and PIR motion sensors, among others. The collected data will be transferred to the user's mobile phone and sent to the users, ensuring crops receive the appropriate quantity of water and optimize irrigation schedules. By monitoring water levels, farmers may detect possible water loss locations and take action to prevent them. The primary goal of our article is to increase productivity, efficiency, and water use through live monitoring, water management, crop growth development, and the potential use of virtual water. Furthermore, breakthroughs in machine learning, such as crop prediction and recommendation, fertilizer detection, and leaf disease detection, will allow for more complex analysis of agricultural data, resulting in even great gains in water management and crop yield. However, it is important to ensure that these technologies are accessible to all farmers, regardless of the location or resources. If giving detailed information on soil properties and crop genetics, machine learning algorithms can forecast agricultural yields and find the best crops for a giving region. Now, farmers can maximize their crops by making data-driven decisions. This one system’s capability to exactly predict crops yields and resources requirements be one of its key advantages. To promote more sustainable and productive farming, farmers should also control the quantity of water and other resources used by them. By assuring a sufficient supply and reducing the likelihood of crops failure, crops forecast, leaf disease diagnosis, and guidance systems can assist address concern related to global food securities in addition to enhancing productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Soil moisture

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Food-Health-Chain: A Food Supply Chain for Internet of Health Things Using Blockchain

Food-Health-Chain: A Food Supply Chain for Internet of Healt...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Das, Puja Haldar, Amrita Singh, Moutushi Pise, Anil Audumbar Guha Roy, Deepsubhra Department of Computer Science HMM College for Women Dakshineswar Kolkata700035 India Department of Computer Science and Business Studies IEM Kolkata700091 India Department of Information Technology IEM Salt Lake Kolkata700091 India Department of Data Science and Machine Learning Computer Science University of the Witwatersrand Johannesburg South Africa Institute of Engineering and Management Kolkata700091 India

ISBN: (纸本)9789819944323

Foodborne disease is still a major public health concern in both advanced and emerging economies. In terms of adopting new technology, the health industry continues to lag behind all other sectors. India has traditionally relied on food farming to make money but which food item is good for whom we have no proper idea. Especially when it comes to healthcare sectors, the Internet of health things (IoHT) and blockchain can play an important role. It can enhance the quality of care for its users and reduce costs by allocating the correct food chain to the targeted users. We are currently employing blockchain in supply chain management to cluster the patients based on their health care type. For example, diabetic patients prefer a particular type of food. Segregating the food type within the supply chain can reduce a lot of effort and cost. Blockchain can provide a solution to the issues that arise in the healthcare-based supply chain system because of its decentralized and immutable nature. The service's execution has been depicted in pictures and accompanied by detailed descriptions. The ultimate result of this study was to demonstrate the immutability, availability, and transparency security of blockchain in the healthcare-based supply chain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments 30

CLEVR-POC: Reasoning-Intensive Visual Question Answering in ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Abraham, Savitha Sam Alirezaie, Marjan De Raedt, Luc Australian Institute for Machine Learning The University of Adelaide Australia Flybits Labs. TMU Creative AI Hub Toronto Canada Örebro Sweden Department of Computer Science KULeuven Belgium

ISBN: (纸本)9782493814104

The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to use existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating all inconsistent ones). Such knowledge often comes in the form of constraints about objects and it tends to be highly domain or environment-specific. We contribute a novel benchmark called CLEVR-POC for reasoning-intensive visual question answering (VQA) in partially observable environments under constraints. In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene. For instance, if one has the knowledge that all cups are colored either red, green or blue and that there is only one green cup, it becomes possible to deduce the color of an occluded cup as either red or blue, provided that all other cups, including the green one, are observed. Through experiments, we observe that the low performance of pre-trained vision language models like CLIP (≈ 22%) and a large language model (LLM) like GPT-4 (≈ 46%) on CLEVR-POC ascertains the necessity for frameworks that can handle reasoning-intensive tasks where environment-specific background knowledge is available and crucial. Furthermore, our demonstration illustrates that a neuro-symbolic model, which integrates an LLM like GPT-4 with a visual perception network and a formal logical reasoner, exhibits exceptional performance on CLEVR-POC. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge management

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Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

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

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. The work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. The results were further converted into graphical representations for enhanced delivery of insights. The study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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