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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms Education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Balamurugan, R. Takale, Dattatray G. Parvez, M. Muzammil Gnanamurugan, S. Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models. © 2024 The Authors

关键词： BiLSTM CNN CWRU dataset Rolling bearings life time

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Lip reading using deep learning and convolutional neural network

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AIP Conference Proceedings 2024年第1期3134卷

作者： Elizabeth Isaac Sachin K. Tomy Basil Joy Edwin Augustine Thomas Hussain Nizar Ahmed Jacob Raybin Assistant Professor Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Lip reading involves deciphering and scrutinizing a speaker’s lip movements to understand spoken words or phrases. The diverse factors like speech pace, intensity, and similar character sequences pose significant challenges to lip reading. This study introduces a lip-reading model tailored for video data containing variable-length sequence frames without audio. Initially, we isolate the lip region from each facial image within the video sequence and amalgamate them into a unified image. Subsequently, a twelve-layer convolutional neural network with two layers of batch normalization is constructed to train the model and extract visual information. This approach aims to mitigate both internal and external disparities encompassing traits such as speaker accent, lighting conditions, picture frames, speaking tempo, and posture. By leveraging extensive data, the model demonstrates the capability to accurately anticipate complete words.

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Unveiling deceit: A computer vision-based approach to detect lies from facial expressions

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AIP Conference Proceedings 2024年第1期3134卷

作者： Elizabeth Isaac Pristy Paul Thoduparambil M. Raghu Rohitha Anuvindha Ansalam T. P. Akshay Assistant Professor Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Polygraph tests monitor heart rate and pulse for truth determination but aren’t fully reliable. Face based deception detection systems are noninvasive and are more convenient. The proposed system uses computer Vision to monitor eye blink rate, gaze direction, lip compression, and pupil dilation to determine truth accuracy in real-time. The number of frames with a threshold value lower than the Eye-Aspect-Ratio (EAR) constant determines the eye blink count. The pupil coordinates are used to track the eye gaze direction. The eye centroid to iris contour maximum distance tracks pupil dilation. Lip compression is determined by measuring the distance between the mean of upper and lower lip landmarks. The weighted sum method is used to find the lie index from the obtained features. The true positive rate (TPR) and false positive rate (FPR) at various threshold values is calculated and an ROC (Receiver Operating Characteristic) curve is plotted using these values. The point on the curve with the highest TPR and lowest FPR determines the optimal threshold for the lie detection system. This method allows customization of the system’s performance to meet specific requirements.

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Keyword search application for videos using frames

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AIP Conference Proceedings 2024年第1期3134卷

作者： Anu Eldho Linda Sara Mathew B. Rohit Eldhobasil Parackal Balu Felix Assistant Professor Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Keyword search in videos is a technique that allows for efficient search and retrieval of specific moments within a video. This is accomplished by using speech recognition technology to convert audio from the video into text. This text can be compared with the visual content of each frame in the video using optical character recognition (OCR) technology. By iterating through each frame and comparing the audio and visual text, matches can be identified and the relevant parts of the video can be quickly accessed using the corresponding timestamps. We use a video analysis tool or library that has the ability to extract timestamps from a video file. These tools can be used to create a list of timestamps for all the frames in a video, which can then be used to quickly access specific moments in the v ideo. This technique can be useful in a variety of applications, such as media search, video editing, and video surveillance.

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Multimodal search in fashion store using CLIP

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AIP Conference Proceedings 2024年第1期3134卷

作者： Isaac Elizabeth Anu Mathew Joby Roy Meckamalil Rotney Sagar V. K. Sruthi N. Kartha Sreekanth P. M. Sreelakshmi Assistant Professor Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Contemporary computer vision systems are typically trained to recognize a limited set of predetermined object categories. This restricted type of supervision limits their applicability and generalization since additional labeled data is needed to indicate any other visual concept. A potential approach that leverages a broader source of supervision involves learning directly from raw text about images. The proposed solution involves implementing a multimodal search in a fashion store using the Contrastive Language Image Pretraining (CLIP) model, which enables vector-based search rather than syntax-based search. CLIP improves product visibility and assists users in obtaining better search results that match their preferences. It is demonstrated that a simple pre-training task of predicting the corresponding image for a given caption is an effective and scalable technique for learning state-of-the-art image representations from scratch using a dataset of (image, text) pairs collected from the internet. Natural language is employed during pre-training to refer to learned visual concepts or describe new ones, enabling zero-shot transfer of the model to downstream tasks. Integration with clustering techniques on the vector space further enhances CLIP’s capabilities, facilitating efficient and accurate retrieval of items. By grouping similar fashion items based on visual similarity and semantic relationships, the system enables efficient and accurate retrieval of relevant items. The model can generalize effectively to most tasks without requiring any training on a specific dataset and is frequently on par with a fully supervised baseline.

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Deep Learning and its Applications: A Novel Approach to Machine Learning with Encoding Algorithms

Deep Learning and its Applications: A Novel Approach to Mach...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Kamalesh Thoppaen Suresh Babu Bhagyashree Ashok Tingare Vijayshri Khedkar Tarun Dhar Diwan P. William Abhishek Badholia Globallogic Inc Santa Clara CA USA Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Deaprtment of Information Technology Sanjivani College of Engineering Kopargaon Amity University Dubai UAE Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing.

关键词： Deep learning Dimensionality reduction Machine learning algorithms Computational modeling Neural networks Feature extraction Data processing Encoding Data models Hyperspectral imaging

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Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Radwa Taha Shereen Afifi Mohamed A. Abd-ElGhany Mohammed A.-M Salem Master Candidate in the Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Assistant Professor in Computer Science Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Professor in Electrical Engineering Faculty of Information Engineering and Technology German University in Cairo New Cairo Egypt Integrated Electronics Systems Lab TU Darmstadt Germany Digital Media Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.

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Automated data preprocessing and training interface for machine learning applications

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AIP Conference Proceedings 2024年第1期2942卷

作者： Anagha Challa Akhila Vutukuri Maithreyi Kenguva Sateesh Kumar Kanagala Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India Assistant Professor Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

Machine learning is a promising tool for analyzing and predicting data. However, it is very time-consuming and challenging to manually process the data. There are also many missing values in the data that can affect the accuracy of the models. We propose an automated approach to pre-process the data. It efficiently performs the various steps involved in the data cleaning process by converting the categorical values to a label encoded value, sampling the data, and replacing the missing values with the appropriate central tendency. This project takes the raw data as input and provides a clean error-free dataset that is suitable for training machine learning models. It also aims to use a web application to automate data pre-processing and training of classification and regression models. It uses multiple algorithms and normalization strategies to get the optimum model based on the desired metric. It allows users to download the clean dataset as well as the trained models after the models have been trained. Multiple datasets, including binary classification and regression, are used to test this model.

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