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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach 5

Revolutionizing Facial Feature Extraction with AI: An Innova...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Lakshmi, B. Alekhya, Jandhyam Lakshmi Parish Venkata Kumar, K. Kishore, B. Kamala Nyni, K. Likhita, K. V.R. Siddhartha Engineering Dept. of Computer Applications KanuruVijayawada 7 India V.R. Siddhartha Engineering College Dept of Computer Science and Engineering KanuruVijayawada 7 India Department of Computer Applications India

ISBN: (纸本)9798350385649

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block. © 2024 IEEE.

关键词： Feature extraction

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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Advanced Machine Learning Techniques for Early Diagnosis of Diabetic Macular Edema 8

Advanced Machine Learning Techniques for Early Diagnosis of ...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Muthuselvi, M. Duvvuri, Suneel Kumar Geetha Rani, K. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Applications Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

One consequence of diabetic retinopathy is diabetic macular edema (DME), a condition that effects the eyes of individuals with diabetes. More than 28 million people been affecting by diabetic macular edema due to diabetes, late diagnose of diabetic macular edema leads to vision loss. Available method with deep learning algorithms and artificial intelligence (AI) for prediction of diabetic macular edema is not effective way for diagnosing in early. Proposed a method with XGBoost algorithm for the classification, pre-processing, extraction and detection of a disease by fundus samples. This method resulted in 0.992, 0.989, 0.991 and 0.990 for accuracy, Recall, precision and F1-score respectively. © 2025 IEEE.

关键词： Deep learning

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Addressing Food Waste: Survey of Approaches, Technological Innovations and Sustainable Solutions 11

Addressing Food Waste: Survey of Approaches, Technological I...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Nandeesh, Talari Rakhra, Manik Chowdary, Pullela Harish Amanpreetsingh Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Lovely Professional University Department of Computer Applications Punjab Phagwara India

ISBN: (纸本)9798350350357

Food waste management in our local is to high for solution. This review article critically examines the complex issue of food waste, analyzing root causes and examining environmental and social impacts. The essay delves into the issue of waste across various phases of the food supply system, specifically concentrating on production, processing, distribution, retail, and consumption stages. Environmental impacts, particularly greenhouse gas emissions and resource scarcity, are scrutinized alongside social consequences such as increased food poverty and hunger. The review evaluates existing strategies and technologies to reduce food waste and proposes comprehensive, long-term solutions. © 2024 IEEE.

关键词： Greenhouse gases

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A Comparative Analysis of Different Machine Learning Techniques For Medical Insurance Premium Prediction 1

A Comparative Analysis of Different Machine Learning Techniq...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Dash, Satyabrata Panigrahi, Bhawani Sankar Sanikommu, Vijay Venkata Bhaskar Reddy Madhavi, B.K. Sahoo, Susanta Kumar Department of Computer Science & Engineering Vishakhapatnam India Vardhman College of Engineeing Department of Information Technology Telangana Hyderabad India Ramachandra College of Engineering Department of Computer Science & engineering Andhra Pradesh Eluru India Indira Gandhi Institute of Technology Department of Computer Science Engineering & Applications Odisha Sarang India

ISBN: (纸本)9798350361780

Predicting health insurance premiums is a crucial task for both insurance companies and policyholders. This paper explores the use of regression approaches to predict health insurance premiums. The study uses a dataset of insurance premiums and related variables, including age, gender, BMI, and location to determine the factors that have the biggest effects on insurance rates. The study found that age and BMI were the main predictors of insurance rates, with gender and location having a relatively little influence. To create predictive models, the study combined various regression approaches, including logistic regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, and linear regression. The predictive models were highly accurate, with a mean absolute error of less than 5% across all examined samples. The findings have significant implications for both insurers and customers, as accurate premium pricing is essential for insurers to maintain their financial health and offer customers coverage at reasonable prices. Accurate premium forecasts help policyholders plan their spending wisely for insurance costs and make informed decisions regarding their healthcare coverage. © 2024 IEEE.

关键词： Health insurance

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Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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A Review of Moving Object Detection Techniques for Night Time 5

A Review of Moving Object Detection Techniques for Night Tim...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Dua, Disha Bhat, Aruna Girdhar, Ashish Delhi Technological University Department Of Computer Science & Engineering Delhi India Kurukshetra University Department Of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350330861

Moving Object Detection in low light conditions is a challenging task due to limited visibility and the presence of noise and objects in the captured images, low contrast, low brightness, low appearance information, and low Signal to Noise Ratio. The majority of night surveillance models suffer from lack of adaptability for different scenes and separation between detection and tracking. This review paper provides a comprehensive survey of recent advancements in Moving Object Detection Techniques for night-time situations. It presents an overview of various approaches proposed in the literature, including traditional and deep learning-based methods, and discusses their strengths and limitations. Moreover, the paper highlights evaluation metrics commonly used in the literature, the datasets they have been evaluated upon, along with a comparative analysis of the performance metrics & various limitations of different methods. Finally, the paper identifies the key research challenges and future directions for Moving Object Detection in Night Time viz. dim-lit environments, such as the need for more robust and efficient algorithms that can handle diverse environmental conditions and real-time applications. © 2023 IEEE.

关键词： Object detection

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Utilizing Artificial Intelligence for Advance Data science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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