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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has b.en designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the sub.equent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 Ieee.

关键词： Convolutional neural networks

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Identification of Therapeutic Plants Using Machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Rohini, S. Keerthana Shree, M.U. Jayalakshmi, R. Malathi, M. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 Ieee.

关键词： Machine learning

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A Transmission and Transformation Fault Detection Algorithm based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign ob.ects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separab.e convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-excitation Networks(SeNet)attention model is fused into the algorithm framework and a new detection scale suitab.e for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SeNet

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A Framework for Video based Sign Language Interpretation Using Machine Learning and Statistical Methods

A Framework for Video Based Sign Language Interpretation Usi...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Swathi D Susmetha K Sreeji. S S. Mangairkarasi B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Hands Deep learning Sign language Translation Tracking Text recognition Speech recognition Streaming media Real-time systems Graphical user interfaces

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brain Tumor Detector Using SVM Algorithm 1

Brain Tumor Detector Using SVM Algorithm

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Aravintharaj, S. Mithileshan, M. Priya, b. B.E Computer Science Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Sri Sairam Engineering College Computer Science Department TamilNadu Chennai India

ISBN: (纸本)9781665476553

In the healthcare sector, disease prediction must be extremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. The brain is the most crucial organ of the human body since it regulates the operation of all other organs and aids in decision-making. It is basically the central nervous system's command post and is in charge of carrying out the body's regular voluntary and involuntary functions. The tumour is an uncontrolled proliferation of undesirab.e tissue that has formed a fibrous mesh inside of our brain. The primary goal is to use this study to detect brain tumours at an initial stages. The study would provide a forecast indicating whether a tumour is present or not. This tool can be used as a significant asset to any medical facility dealing with brain Tumor. Using machine learning and deep learning algorithms enab.es the detection of brain tumour's. We use the algorithm Support Vector Machine (a supervised machine learning approach that can be utilized for both regression and classification prob.ems issues) out of all the machine learning algorithms since it has a greater precision and is simple to train on big datasets. In this study, the histogram of oriented gradient is used to collect the feature information from medical pictures of patients tumour's stored within the database. SVM is then used to separate these photos into tumour and non-tumor pictures. by using this method, it is possib.e to predict brain tumour's quickly and with a high degree of accuracy (95%), which aids in patient treatment. © 2022 Ieee.

关键词： Tumors

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Image Super-Resolution Using Auto encoders

Image Super-Resolution Using Auto Encoders

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International Conference on Trends in electronics and Informatics (ICeI)

作者： K. Sandhya Rani Pancheti bhavya R. Vignesh e. Murali D. Deepa J. Cruz Antony B.E Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Image super-resolution (SR) remains a crucial prob.em in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoders, a specific kind of neural network whose task is to learn compressed representations of the high resolution images. In the context of the proposed model, there is an encoder, which, alternately, compresses the input and a decoder which reconstruct the output is proposed as capturing as well as reproducing all the details of an image. experimental results show that the proposed method is one of the most efficient and accurate ones on benchmark datasets in terms of better details preservation and artefact reduction besides reducing processing time compared to the conventional methods. This makes this approach to be flexib.e and efficient to be used in medical imaging, satellite and digital photography making it an effective solution to improving image quality and resolution.

关键词： Image quality computer vision Visualization Image coding Superresolution Neural networks Signal processing algorithms Signal processing Optical flow Image reconstruction

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Private Document Vault with Server-Side encryption in Cloud AWS S3 bucket

Private Document Vault with Server-Side Encryption in Cloud ...

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International Conference on Communication and electronics Systems (ICCeS)

作者： Daliparthi Jeevana Aditya Sunki Laxmanraj R. Sathya bama Krishna Computer Science Engineering (B.E) Sathyabama Institute of Science and Technology Sath Sathyabama Institute of Science and Technology

The most requested future-oriented innovation in the world is distributed computing. Distributed storage is one of the most famous managements presented in cloud computing. Information is stored on various external servers as opposed to the fixed servers used in traditionally located information capacity in distributed storage. All the information stored on various external servers leaves the client unattended, and no one knows exactly where the information is stored. Rejected by distributed storage vendors who claim they can protect your information, but no one trusts them. Information stored via the cloud and traveling in cleartext design within an organization is a security risk. This research study proposes a strategy for allowing clients to securely store and access information from distributed stores. This technology ensures the security and protection of information stored in the cloud. Another advantage of this strategy is that it is encrypted using cryptographic methods. In this technology, the files are encrypted using both symmetric and asymmetric algorithms, which is nothing but Hybrid encryption and as a result the end user will get a pair of keys that will help in decrypting the files. If a data b.each occurs the first layer of security will get damaged but the second layer of security helps in saving the files from data b.each.

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brain Tumor Detector Using SVM Algorithm

Brain Tumor Detector Using SVM Algorithm

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International Conference on Computational science and Technology (ICCST)

作者： Aravintharaj. S Mithileshan. M b. Priya B.E Computer Science Engineering B.E Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu Computer Science Department Sri Sairam Engineering College Chennai TamilNadu

ISBN: (纸本)9781665476560

关键词： Support vector machines Machine learning algorithms databases Scientific computing Magnetic resonance imaging Predictive models Classification algorithms

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics 4

Proactive Network Optimization Using Deep Learning in Predic...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu bhat, Niyati Kanna, b. Rajesh Rajalakshmi, S. Jeyavathana, R. beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Prob.ems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this prob.em. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. effective modeling is enab.ed by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliab.e forecasting of future IoT traffic trends. Mean Squared error (MSe) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 Ieee.

关键词： Time series analysis

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Identification of Therapeutic Plants Using Machine Learning

Identification of Therapeutic Plants Using Machine Learning

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Rohini S Keerthana Shree M.U Jayalakshmi R Malathi M L. Jabasheela M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

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