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A Secure Approach to Avoid Data Repetition in Cloud Storage Systems 2

A Secure Approach to Avoid Data Repetition in Cloud Storage ...

引用

2nd international conference on Artificial intelligence and machine Learning Applications, AIMLA 2024

作者： Swedhaa, M.A. Sathya, V. Vanitha, M. Jothilakshmi, T. Kongunadu College of Engineering and Technology Artificial Intelligence and Data Science Trichy India Mohamed Sathak A.J. College of Engineering Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350349221

Cloud Storage will be current data research and data management field in terms of security and elimination of repeated data-sets. In simple terms, this current research introduces a strong system called "Cloud-SecureDedupe(C-SD)". to make sure data is safe and cloud storage works well. The system helps with a common problem called data repetition, where the same information is stored more than once. "Cloud-SecureDedupe (SDP)"uses a safe method to solve this issue, making sure your data is reliable in cloud storage. It adds extra protection by using advanced techniques to keep important information secure. Additionally, it smartly identifies and removes any repeated data, not only making your data more secure but also saving space in the storage, so it's used more efficiently. The system has been carefully tested and proven to work well, ensuring that your experience with cloud storage is both safe and smooth. © 2024 IEEE.

关键词： Cloud storage

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Multimodal machine Learning for Early Detection of Autism in Children: Integrating Gestures and Mood Analysis 2

Multimodal Machine Learning for Early Detection of Autism in...

引用

2nd DMIHER international conference on Artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Puri, Chetan Ghodeswar, Ujwala Reddy, K.T.V. Sharma, Mansi Warudkar, Sahil Department of Computer Science and Design Wardha India Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Nagpur India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331528713

Autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child's behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child's emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results. © 2024 IEEE.

关键词： Diseases

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2024 IEEE 2nd international conference on Image Processing and Computer Applications, ICIPCA 2024

2024 IEEE 2nd International Conference on Image Processing a...

引用

2nd IEEE international conference on Image Processing and Computer Applications, ICIPCA 2024

ISBN: (纸本)9798350360240

The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machine learning and data mining;research on real-time data transmission and signal processing system of CIM practical training teaching platform based on 5G network;design of systematic financial risk warning system based on integrated classification algorithm;development of a multi-objective optimization framework for submersible localization and search operations;improved thermal dome structure optimization design based on neural network algorithm;transmission line inspection image intelligent diagnosis system;and application of computer artificial intelligence infrared image processing technology in strength detection of building steel structure.

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Real - Time Speech Decoding through Advanced lip Reading using Deep Learning Model 2

Real - Time Speech Decoding through Advanced lip Reading usi...

引用

2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Tarun, M. Bairavel, S. Sudha, D. John, Jobin Vignesh, G. KCG College of Technology Department of Artificial Intelligence And Data Science Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

This research aims to develop a real-time speech decoding system by advanced lip-reading techniques through a deep learning model. The proposed solution integrates cutting-edge advancements in deep learning to make improvement in the efficiency of speech recognition systems. By employing this model will be trained on large datasets of audio-visual speech samples to learn analyzing the patterns of lip movements corresponding to speech phonetics. The system's real-time capability will be achieved through optimized model architectures and efficient computational strategies. The implementation will be validated using diverse datasets and evaluated against standard benchmarks, demonstrating its potential for applications in noisy environments, accessibility for the hearing-impaired, and improved speech recognition in various real-world scenarios. This research contributes to the evolving field of multimodal deep learning, offering a promising avenue for real-time speech decoding through advanced lip reading. © 2024 IEEE.

关键词： Contrastive Learning

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Cloud Based Attendance Monitoring System Using MobileNet SSD 2

Cloud Based Attendance Monitoring System Using MobileNet SSD

引用

2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Abirami, M.S. Khachane, Varun Joshi, Aadarsh SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350365269

In order to improve the accuracy of attendance tracking in institutional and workplace settings, a dual authentication system is intended to be implemented. The technology ensures secure and accurate attendance recording by combining facial recognition and One-Time Password (OTP) verification. Users are first verified using facial recognition, and after a successful verification they are given a special OTP for extra security. A new system for detecting and tracking objects in real time using multiple pan-tilt cameras on unmanned platforms is proposed. This system utilizes the MobileNet-SSD algorithm, which enables real time, GPU- free human object identification on mobile platforms. By precisely distinguishing and following articles, the framework can work on the productivity and viability of different applications, like reconnaissance, observing, and search and salvage activities. Moreover, the data storage is done in cloud to enhance the access of images more effectively and efficiently. Furthermore, the framework can be utilized to upgrade participation records by decreasing participation extortion, advancing responsibility in various settings. © 2024 IEEE.

关键词： Computer vision

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Challenges and Findings in Creating Smart Assistants for Mixed Reality Training Apps 2

Challenges and Findings in Creating Smart Assistants for Mix...

引用

2nd international conference on Hybrid Human-Artificial intelligence (HHAI)

作者： Chuan, Ching-Hua Soriano, Erik Univ Miami Sch Commun Interact Media Coral Gables FL 33124 USA

ISBN: (纸本)9781643683942;9781643683959

Mixed Reality (MR) headsets such as Microsoft's HoloLens have not only revolutionized the immersive experience for entertainment but also been adopted by various industries to improve work procedures. One of the particularly popular types of MR apps among industries is the training app. In this pilot study, we created and tested a prototype to expand such MR training apps from simply being a hologram manual to an assistant that recognizes if the user provides the task correctly. We first obtained feedback from participants in a user study regarding the design, usability, and experience of the app. We then fed the videos recorded in the user study to train an object recognition model via transfer learning. In addition to participants' positive feedback on the design and promising results on object recognition, we also identified unexpected but important issues for MR app design and programming, including trade-offs between the user's workflow and the accuracy of the smart assistant.

关键词： Mixed reality artificial intelligence object recognition human factors

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Application of machine Learning Algorithms for Analyzing Sentiments using Twitter dataset 2

Application of Machine Learning Algorithms for Analyzing Sen...

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2nd international conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Rewatkar, Bhairavi Barhate, Aditya Verma, Prateek Datta Meghe Inst Higher Educ & Res Fac Engn & Technol Dept Artificial Intelligence & Data Sci Wardha 442001 Maharashtra India Datta Meghe Inst Higher Educ & Res Fac Engn & Technol Dept Artificial Intelligence & Machine Learning Wardha 442001 Maharashtra India

ISBN: (纸本)9798350391558;9798350379990

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs machine Learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naive Bayes, Decision Tree, Logistic Regression, Support Vector machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

关键词： Natural Language Processing Sentiment Analysis machine Learning Deep Learning Social Media Analysis

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Elevating Assessment: Advancing Short Answer Evaluation Through Embedding and Text Alignment 2

Elevating Assessment: Advancing Short Answer Evaluation Thro...

引用

2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Wagdy, Muhammed Khaled, Farah Saeed, Mostafa Mansour, Hesham Gomaa, Wael H. University for Modern Sciences and Arts Faculty of Computer Science Cairo Egypt Beni-Suef Universityy Faculty of Computer and Artificial Intelligence Egypt

ISBN: (纸本)9798350362633

This paper addresses the critical challenge of automating short answer grading, an increasingly essential task in educational settings. We present a novel approach that integrates the Abydos package, which includes 171 string similarity algorithms, with a machine learning framework utilizing a Random Forest regressor. This combination enables the efficient and effective evaluation of student responses by extracting and utilizing textual features systematically and achieved a 60.43% correlation score on the Texas Data structure dataset. Our experiments demonstrate the practicality and superiority of this methodology in enhancing grading consistency and reducing educators' workload. The findings highlight significant improvements in grading automation, showcasing its potential to facilitate and transform educational assessment practices. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Beck's Depression Inventory and Emotion Detection using GAN for Comprehensive Depression Assessment and Intervention 2

Integrating Beck's Depression Inventory and Emotion Detectio...

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2nd international conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Poongodi, K. Nivesh, R. Abhi Jeberson, J. Q. Blessed Divakar, V. S. Gowshikan, S. Sri Eshwar Coll Engn Dept Informat Technol Coimbatore Tamil Nadu India Sri Eshwar Coll Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

This research study suggests a novel approach to measure depression levels by integrating emotion recognition using Generative Adversarial Networks (GAN) and Beck's Depression Inventory (BDI). The challenge encountered here, was that relying solely on facial recognition could lead to inaccurate results, as individuals may manipulate their expressions. To address this, we integrated BDI score assessment to supplement emotion detection. Additionally, we faced the challenge of ensuring the authenticity of facial recognition amidst potential manipulation. Therefore, we implemented face recognition for each BDI question, ensuring accurate emotion detection by the completion of the questionnaire. Our technique combines state-of-the-art AI-driven emotion analysis with the widely used BDI psychological evaluation tool to offer a comprehensive and multimodal approach in diagnosing depression. This method provides a more complete image of the person's emotional state by analyzing self-reported BDI scores and emotional data produced by the GAN model. A thorough and accurate assessment of the degree of depression is produced when these tests are combined. Further, the system provides customized activities and intervention strategies for people with varying degrees of distress based on the determined depression level. This study represents a significant advancement in the use of technology for extensive mental health screening and treatment.

关键词： Depression assessment Emotional expression recognition Generative adversarial networks Beck's depression inventory Artificial intelligence Degree of depression

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Integration and Analysis of Network Information System Operation and Maintenance Data based on Artificial intelligence Technology 2

Integration and Analysis of Network Information System Opera...

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2nd IEEE international conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Wang, Jian Zhang, Xuxia Lu, Zhenwei Xie, Qingyu Jiang, Tongjun State Grid of China Technology College Shandong Jinan China

ISBN: (纸本)9798331504960

Nowadays, the operation and management of network information systems face numerous challenges, especially in data management and analysis. This study aims to improve the data integration and analysis process of network information system operation and maintenance through artificial intelligence(AI) technology. First, the study intelligently classifies and preprocesses operation and maintenance data by applying machine learning(ML) algorithms, extracts key information using natural language processing techniques, and subsequently conducts in-depth data correlation analysis using a deep learning(DL) model, so as to effectively identify potential bottlenecks in the system and early warning failure signals. The aspect of improving the learning rate when parameter tuning increases the accuracy from 88% to 90%, indicating that the faster learning rate helps the model to better adapt to the data, although there may be a risk of overfitting. The findings of this paper will provide new ideas for the operation and maintenance of networked information systems and will be useful for the improvement of the operation quality of other information systems. © 2024 IEEE.

关键词： Data accuracy

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