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Personal Exercise Trainer Using computer Vision 2

Personal Exercise Trainer Using Computer Vision

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Hari Ram, S. Ashok, A. Ramalingam, A. Dheenadhayalan, S. National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798350328202

The Personal Trainer online application tracks users progress and offers realtime feedback to encourage users in their daily workout routines. To identify and analyze human body movements, the program uses the mediapipe library, which integrates opencv and machine learning methods. The Personal Trainer online app provides an engaging and immersive workout experience by incorporating these technologies. The application supports several exercises, including weightlifting, squats, bicep curls, and pull-ups. The software uses the webcam on the computer to record realtime footage of the user completing the workout after they choose it. The user's body motions are then recognized from the recorded video using opencv and mediapipe, and performance-based feedback is given. Users may quickly move through the various training modes, check their activity history, and measure their overall progress thanks to the app's user-friendly and intuitive interface. © 2024 IEEE.

关键词： computer vision

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The Tree Autoencoder Model, with Application to Hierarchical Data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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TraceChainBD: Securing Supply Chains from Counterfeiting in Bangladesh with Blockchain and IoT Integration

TraceChainBD: Securing Supply Chains from Counterfeiting in ...

引用

2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Karmoker, Sagar Khan, Md. Adib Lamia, Sanjida Alam Tanjid, Mobinul Ahasan Dihan, Shahriar Sadman Anik, Nur Adnan Chowdhury East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

The scheme uses blockchain technology in Bangladesh's supply chain to eliminate counterfeit products, improve tax compliance, and build consumer trust. By ensuring product authenticity, it safeguards brand reputations and boosts government revenue. Manufacturers, distributors, and retailers can enter product data which will then generate QR codes. In case of receiving the product or checking its authenticity, they can scan these QR codes via their mobile or a dedicated IOT device and all the information will be stored in Blockchain. Since blockchain data is secure and immutable, it cannot be altered, consumers can easily check product details by scanning QR codes, and if a product is flagged as suspicious, the Mobile Court Management System can trace its origin. This blockchain-based approach ultimately helps prevent counterfeit goods from entering the market and enhances the government's ability to collect taxes. © 2024 IEEE.

关键词： Blockchain

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Biomimetic and Biological Sensing Technologies for the Assessment of Water Contaminants 1

Biomimetic and Biological Sensing Technologies for the Asses...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Thaluka, Mohan Raj Maheswari, K. Nuthanakanti, Bhaskar Srujan Raju, K. Jonnadula, Narasimharao Okali, Rakshita Dept. Computer science and engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9798350367720

Biological sensors, including biosensors and biomimetic sensors, have gained significant attention in recent years due to their exceptional capabilities in detecting and monitoring pollutants in water environments. These sensors employ biological components such as bacteria, Fungi, enzymes, antibodies, microbial cells, or biomimetic materials to recognize and quantify specific contaminants to pathogens, with high sensitivity and selectivity. This review provides an in-dept. analysis of the current state-of-the-art in biological sensor technology for water quality assessment. It discusses the underlying principles of biological sensing mechanisms and explores recent advances in sensor fabrication, including materials, immobilization techniques, and signal transduction methods. Moreover, the review examines the diverse applications of biological sensors in water monitoring, ranging from detecting chemical pollutants to assessing microbial contamination. Additionally, challenges such as stability, reproducibility, and field deployment are addressed, along with future directions for research and development to further enhance the performance and applicability of biological sensors in safeguarding water resources and public health. Here, we employed three models - SVM, logistic regression (LR), and random forest (RF) - to determine the biological and chemical components of water, such as bacteria, fungi, enzymes, antibodies, microbial cells, or biomimetic materials, in order to determine whether it is being utilized for drinking water. Because there are fewer datasets available for biological and chemical components, the dataset was created using machine learning (ML) models. Three machine learning models were used to calculate water pollutants percentages, with SVM proving most effective for irrigation and domestic water, and LR for drinking water, resulting in accurate water supply. © 2024 IEEE.

关键词： Antibodies

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers 1

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Sarkar, Subhasish Kumar, Gautam National Institute of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer. © 2024 IEEE.

关键词： Plant diseases

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Comprehensive Testing Strategies for Water Purity by Assessing and Eliminating Viruses and Bacteria using Machine Learning algorithms

Comprehensive Testing Strategies for Water Purity by Assessi...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Thaluka, Mohan Raj Maheswari, K. Srujan Raju, K. Nuthanakanti, Bhaskar Pavan Kumar, G. Santhuja, P. Cmr Technical Campus Dept. Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350366846

Water, essential for life, is often contaminated with bacteria and viruses, depriving many accesses towards purified drinking and irrigation water. The presence of pathogens in unpurified water puts human health at risk and contaminates crops, leading to waterborne illnesses. Detecting indicator bacteria and viruses in water is crucial for assessing microbiological quality. Despite efforts by regulatory authorities, disease outbreaks linked to drinking water persist globally. The World Health Organization released some threshold values based on some water parameters. The metrics are named as The Water Purity by Assessing and Eliminating Viruses and Bacteria (WPAEVB) and Irrigation WQI (IWQI) which can measure the water quality. This paper proposed a network architecture to analyze all the parameters by using machine learning tools (ML) tools which will determine the drinking water and irrigation water based on virus and bacteria microbiological values. The model is developed based on LoRa and land topology. Here we used three models SVM, logistic regression (LR), and random forest (RF) to know whether irrigation water is being used for drinking water by detecting the percentage and levels of bacteria and virus. The dataset was developed based on the ML models due to the lower availability of datasets related to irrigation and drinking water and bacteria and virus percentage is also calculated by using three models. Following the application of every model, SVM produced the greatest results for irrigation water, while LR produced the best performance for drinking water. The three ML models were applied in order to complete the iterative feature elimination. © 2024 IEEE.

关键词： Bacteria

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Deciphering Deception: Unmasking Fake News in Multilingual Contexts 5

Deciphering Deception: Unmasking Fake News in Multilingual C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agarwal, Ashutosh Singh, Yash Pratap Rai, Vipin Galgotias University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

A major global worry in the ever-expanding digital ecosystem is the spread of incorrect information. The ability to recognize false news in non-English languages is still lacking, despite great advancements in the identification of false news in English. This research looks at the unique opportunities and problems that these languages bring. It offers a thorough assessment and comparative analysis of existing approaches, highlighting their advantages and disadvantages. By doing comparative study of various differ techniques we have found that Multilingual-Fake Model currently works the best. The results of the study highlight how critical it is to use inclusive and linguistically varied tactics in order to successfully counter disinformation. It emphasizes how crucial it is to create trustworthy tools and processes that can adjust to the subtleties of many languages and their socio-cultural settings. It highlights how crucial it is to comprehend these components completely in order to create detection methods that are more potent. The study admits that the dissemination of incorrect information is an issue that is deeply rooted in the social environment of linguistic groups and transcends language borders. A discussion of possible future study areas closes the report. In a multilingual culture, this study marks a substantial breakthrough toward the development of more thorough and efficient techniques for spotting bogus news. © 2024 IEEE.

关键词： Bidirectional Transformer fake news multi-lingual

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