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Recommendation System For Social Platform Using Deep Learning Techniques 8

Recommendation System For Social Platform Using Deep Learnin...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nishchal Narayan, S. SriVidya, M.S. Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations (Google, Explorer, Microsoft Edge), music recommendations (Wynk), video recommendations (YouTube), movie recommendations (Amazon Prime), and product recommendations (Flipkart, Amazon). The study employs similarity coefficient calculations like Jaccard Distance and Cosine Similarity, alongside ranking measures such as PageRank. Furthermore, the system's performance is evaluated using advanced deep learning techniques, including Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Comprehensive experimentation involves measuring the F'l-Score and comparing the accuracy of various machine learning and deep learning algorithms to identify the most effective methods for friend recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms 3

Deep Learning Techniques for Bangladeshi Coin Detection and ...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Babor, Ashraf Hussan Choity, Umme Habiba Kaspia, Most. Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Hamid, Md. Ekramul Qadirabad Dayarampur Dept. of Computer Science and Engineering Natore6431 Bangladesh University of Rajshahi Department of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Efficient currency detection and counting systems are crucial for the smooth operation of any financial ecosystem. This paper explores the development, implementation, and potential impact of a specialized currency detection and counting system for Bangladeshi coin currencies. Through this comprehensive study, this paper addresses the unique challenges and opportunities in Bangladesh's coin currency management landscape, highlighting the motivations and results of such a system's introduction. computer vision technology includes currency detection. In this paper, we present a coin currency detection system that can detect coin currency from images. Designing a CNN-based coin recognition system for the recognition of Bangladeshi coins of denominations '1tk', '2tk', and '5tk'. We have created a dataset that contains more than 10,000 images. We photographed coin currencies from both sides, and the system can recognize coin currencies from both sides. For high efficiency, we test on various backgrounds. Experimental results are presented successfully with a training accuracy of 99.99% with VGG16, 99.38% with VGG19, and 99.99% with EfficientNetB0. We also got validation accuracy with VGG16 of 90.90%, 99.96% with VGG19, and 99.51% with EfficientNetB0. © 2024 IEEE.

关键词： Deep learning

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Genetic Data for Early Detection and Treatment of Cystic Fibrosis through Machine Learning 8

Genetic Data for Early Detection and Treatment of Cystic Fib...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deepthi, P. Bhuvaneshwari, M. Chithra, K. Dhanasree, L.P. Jananee, V. Layola, J.A. Adlin Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Cystic Fibrosis (CF) is a genetic disorder that significantly impacts the respiratory and digestive systems, requiring early detection and precise management to improve patient outcomes. This research study presents a machine learning model designed to predict the presence, severity, and potential treatments for Cystic Fibrosis using genetic data. By leveraging collaborative filtering techniques, our model not only identifies individuals at risk but also enables personalized treatment plans based on genetic predispositions. The proposed model aims to revolutionize Cystic Fibrosis diagnosis and management by automating the screening process on a mass scale. This automation facilitates the identification of demographic areas and genetic populations with a higher susceptibility to Cystic Fibrosis, enabling targeted interventions. Through large-scale genetic data analysis, our approach ensures that at-risk populations receive timely and appropriate care, ultimately improving the quality of life for Cystic Fibrosis patients. The findings suggest that the implementation of this predictive model in clinical settings can lead to earlier and accurate diagnoses, personalized treatment plans, and significant advancements in Cystic Fibrosis care. The implications of this research extend beyond Cystic Fibrosis, offering a framework for applying machine learning in the diagnosis and treatment of other genetic disorders. © 2024 IEEE.

关键词： Personalized medicine

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A Survey on smoking and Alcohol Tracking System: State of the Art, Analysis, and Performance Evaluation 2

A Survey on smoking and Alcohol Tracking System: State of th...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Velavendan, R. Shubram, S. Shyam Sundar, V. Padmavathi, B. Kavitha, D. Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Addiction to alcohol and tobacco is still a major worldwide health issue that requires innovative approaches to early detection and monitoring. This review of the literature provides a thorough summary of recent research projects in the area of tracking systems for alcohol and tobacco use that are both intrusive and non-intrusive for correct classification. The main goal of this research is to investigate how different IOT-based models might improve the precision and effectiveness of alcohol and smoking detection. The measurement of several IoT models used in tracking systems for alcohol and smoking makes up a sizeable section of this work. The study assesses the benefits and drawbacks of each model. Additionally, how effectively it can identify individuals who smoke or drink and how well it can work with various datasets. This research intends to direct future research efforts and encourage the development of more precise and effective tracking systems for smoking and alcohol use, ultimately resulting in improved public health outcomes. © 2024 IEEE.

关键词： Internet of things

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Segmentation and Classification of Skin Lesions Using Ensemble of Convolutional Neural Networks

Segmentation and Classification of Skin Lesions Using Ensemb...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Rahman, Md. Mahfuzur Chandra Shill, Pintu Khulna University of Engineering and Technology Dept of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

This research proposes an advanced ensemble model based on Convolutional Neural Networks (CNNs) designed to enhance the segmentation and classification of skin lesions, a critical step in the early detection of melanoma and other skin cancers. The study explores ensemble strategies, including bagging, boosting, and stacking, to identify the most effective approach for combining multiple CNN models. A novel ensemble method incorporating pre-trained dual encoders is introduced to improve segmentation accuracy by extracting critical features and precisely delineating lesion boundaries. The dual encoders are configured with different encoder-decoder architectures, activation functions, and optimization algorithms to optimize feature extraction and segmentation performance. The suggested model was tested on the ISIC 2019 and HAM10000 datasets, attaining an accuracy rate of 95.3%, with sensitivity at 93.7%, specificity at 96.8%, and an F1-score of 94.5%. The findings demonstrate a significant improvement over current leading techniques, confirming the robustness and reliability of the proposed ensemble strategy. The superior performance of the model suggests its potential for integration into clinical practice, providing dermatologists with a valuable tool for early skin cancer detection and improving patient outcomes through timely intervention. © 2024 IEEE.

关键词： Convolutional neural networks

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Ethereum Powered E-Tendering System

Ethereum Powered E-Tendering System

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Ananthajothi, K. Jaisuriya, P.K. Haribala, S. Rajalakshmi Engineering College Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

The use of information technology to trade information, deliver services, and other things is becoming more and more commonplace in the era of technological advancement. Tendering is one such area where the public and private sectors collaborate. E-tendering is a procurement technique that uses the internet and digital technologies to automate and simplify the whole bidding or tendering process. This makes it easier for the bidders to browse through all of the open tenders and select one to proceed with. But it frequently results in a number of challenges that both bids and bidders must deal with. The system is inefficient because of scale and transparency problems brought on by its centralized architecture. Additionally, the system frequently shares information about tenders with other outside organizations, which compromises the confidentiality and integrity of the tenders. To manage these issues, a decentralized method is used in the deployment of the Blockchain e-tendering system. Consequently, the procurement procedures will have the opportunity to leverage the potential of blockchain technology to enhance security and transparency. This research aims to provide an electronic tendering system that is transparent, liquidate, and safe. It will manage tender allocation and use smart contracts to automate the underlying processes with the least amount of human interaction possible. © 2024 IEEE.

关键词： Smart contract

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