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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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Real Time Flower Identification by Artificial Intelligence 4

Real Time Flower Identification by Artificial Intelligence

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4th international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Rahman, Moshiur Joy, Taushik Ahmed Akter, Samia Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. Machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily. © 2024 IEEE.

关键词： Artificial Intelligence data science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Transfer Learning based Human Activity Recognition in Video data 15

Transfer Learning based Human Activity Recognition in Video ...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Gupta, Saurabh Mahapatra, Rajendra Prasad Computer Science and Engineering Department SRM Institute of Science and Technology Delhi NCR Campus India

ISBN: (纸本)9798331300579

Due to its potential uses in a number of industries, including healthcare, security, and entertainment, human aberrant activity identification has attracted a lot of interest lately. Real-time detection and classification of anomalous activity can aid in prompt intervention and personal safety by allowing for appropriate action. In this work, we give a summary of current developments in the identification of aberrant human activity. In this section, we go through the numerous kinds of sensor data that are employed in this field of study, including video data and data from accelerometers and gyroscopes. We also discuss the difficulties in creating reliable and accurate models that can detect aberrant human behavior. The absence of labeled data, which makes it difficult to train models that generalize well to multiple settings, is one of the main obstacles in the detection of human aberrant behavior. The use of transfer learning and few-shot learning strategies has been the focus of current research to address this problem. We also talk about how human anomalous activity recognition might be used in a variety of contexts, such as fall detection in senior care, spotting strange conduct in public places for security, and boosting virtual reality immersion. Overall, the ability to recognize human aberrant behavior is a crucial area of study that has the potential to have a big influence in a number of different industries. For the purpose of creating more precise and reliable models that may be used in real-world circumstances, further study in this area is required. © Grenze Scientific Society, 2024.

关键词： Video analysis

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Cardiac Pulse Monitoring Through Multi-Scale Spectrum

Cardiac Pulse Monitoring Through Multi-Scale Spectrum

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2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Yadav, Pranav Choubisa, Mudit Shukla, Anuj Radhika, R. School Of Computing Srm Institute Of Science And Technology Department Of Networking And Communications Chengalpattu Kattankulathur603203 India

ISBN: (纸本)9798350371567

Advancements in medical technology have paved the way for non-invasive methods of assessing vital parameters, marking a significant innovation in healthcare. This research addresses the challenge of contactless healthcare detection by introducing a novel approach that combines Multi-Scale Spectrum Filtering (M2SF) Photoplethysmography (PPG) and Videoplethysmography (VPG) techniques for estimating heart rate (HR) using RGB data. The proposed method eliminates the need for physical contact, offering a convenient solution for monitoring HR in various settings, including home environments. In experimental trials, the method demonstrates promising performance akin to wearable devices. To validate its accuracy, comparisons are drawn with established devices such as a Holter monitor and a commercial smartwatch, both renowned for extracting ECG data. Evaluation under scenarios with known or unknown average heart rates reveals minimal deviation, with HR estimation accuracy reaching 85% to 90% when considering lifestyle and HR information. This research highlights the effectiveness and reliability of the proposed approach, showcasing its potential for widespread adoption in remote healthcare monitoring and management. © 2024 IEEE.

关键词： Photoplethysmography

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Detection of Phishing Attack Using Machine Learning

Detection of Phishing Attack Using Machine Learning

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international conference on advances in data science and computing technologies, asdc 2022

作者： Rastogi, Uttkarsh Tanu Singhal, Vinayak Gupta, Ankush Kumar, Vimal Department of Computer Science and Engineering MIET Uttar Pradesh Meerut250005 India

ISBN: (纸本)9789819936557

It is thought that a spate of phishing scandals first occurred in the mid-1990s, when a group of criminals pretended to be AOL employees and used instant messaging and email to purloin users’ passwords and hijack their accounts. Today with the increasing use of the internet, cybercrime is an easy way to steal sensitive information from users. Attackers are keenly aware of important user information such as name of the user, their password and bank details. This paper identifies criminal URLs for stealing sensitive information by finding various features in URLs. With the use of databases in training our model, it will provide a distinction between a website for identity theft and sensitive information. The aim of our paper is to find the best algorithm for training and training in the largest database that provides the most accurate result. Ways to find criminal websites for stealing sensitive information are being developed, divided into three categories: list-based, heuristic methods and machine learning methods. The result also shows that the accuracy of the detection of criminal websites for stealing sensitive information increases as additional databases are used as training databases. All dividers do well when 90. Our work analyzes various machine learning strategies when applied on a database of related attribute websites and their associated information that might be helpful in locating a website could be a crime for stealing sensitive information. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

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A Framework for E-Auction Scheme usingHyperledger Fabric 14

A Framework for E-Auction Scheme usingHyperledger Fabric

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Thallapelli, Tejasri Cheripelli, Ramesh Dept of IT G Narayanamma Institute of Technology and Science Hyderabad India

In these days most of the people has started using e-commercebased applications they become very much handy. E-Auction are among the most inuential e-business application. There are so many dierent se- curity problems in implementing the e-auction such as, the privacy of sellers and bidders, fairness in the transactions, transaction data secu- rity, and veriability. The Hyperledger fabric blockchain architecture is one of the most widely used permissioned blockchain designs, and it has made a substantial contribution to the development of a framework for the safe conduct of online auctions;it ensures data integrity, reliability, and transparency while reducing the corruption, data manipulation, and uncertainty that are commonly encountered in online systems. © Grenze Scientific Society, 2023.

关键词： Smart contract

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Identification of Rock Images in Mining Industry: An Application of Deep Learning Technique

Identification of Rock Images in Mining Industry: An Appli...

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international conference on advances in data science and computing technologies, asdc 2022

作者： Paty, Soumyadeep Kamilya, Supreeti Department of Mining Engineering Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789819936557

A major part of economic upswing of a country depends on the mining industry. An exhaustive research is being held worldwide to make mining easy and cost effective. The present study deals with different types of rock identification using deep learning approach. Rock identification is considered to be an effective mining of minerals. In general, rocks are identified by skilled and expert professionals who can recognize the rocks by visual identification, spectroscopic methods, chemical analysis, etc. However, these methods are costly and time consuming. In this work, we propose a convolutional neural network (CNN) architecture to identify different types of rocks like igneous, sedimentary and metamorphic. Moreover, we classify 8 subtypes of the above rocks, such as basalt and granite (Igneous rocks);coal, limestone, sandstone and shale (sedimentary rocks);marble and quartzite (metamorphic rocks). The dataset of rock images, collected from different parts of India, are considered in the study. The proposed CNN is compared with an already established CNN—VGG16 and another established CNN model. The accuracy of our proposed model is higher compared to the other two models in case of both 3-class and 8-class classifications. Hence, the results show superiority for the proposed architecture. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Optimizing Fog computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Detection of Chaotic Cellular Automata Using Convolutional Neural Networks: A Comparative Study

Detection of Chaotic Cellular Automata Using Convolutional N...

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international conference on advances in data science and computing technologies, asdc 2022

作者： Kamilya, Supreeti Paty, Soumyadeep Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Mining Engineering Kazi Nazrul University West Bengal Asansol India

ISBN: (纸本)9789819936557

For years, researchers have involved themselves to study chaos using various tools. Cellular automaton (CA) is known to be an excellent tool to study chaos because of its discrete dynamical nature. Cells of classical cellular automata (CAs) follow same function to change their states. Whereas in non-uniform CAs, all the cells may not follow same function for evolution. Therefore, it is very much difficult to study chaos in non-uniform CAs. Also, any combination of chaotic functions of classical CA may not exhibit chaotic pattern. So, in this study, we deal with non-uniform CAs where the cells follow the chaotic rules. Such CAs show chaotic as well as non-chaotic behavior. The present work generates snap-shots of space-time diagrams of the CAs. The diagrams or the images are given as the input to four different convolutional neural network architectures, such as, AlexNet, VGG 16, GoogleNet and ResNet, in order to classify choatic and non-chaotic CAs. A comparative analysis is given in the work that shows that the accuracy obtained by ResNet 18, for the classification, is 81.01%, which is the highest among all the other models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cellular automata

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Location-Based Geographical-Spatial data Mining for Detection and Visualization of Reported Problems using Image Processing Techniques 14

Location-Based Geographical-Spatial Data Mining for Detectio...

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Harsoor, Anita Soma, Bhavani P D A College of Engineering Department of Computer Science and Engineering Kalaburagi India

We face many possible problems/issues in our daily lives, such as potholes, garbage filling, uncomfortable parking, broken roads, etc. To report that kind of problem becomes a great hassle. To solve this problem we developed a Geographical-Spatial data Mining System (GDMS), an android mobile application named Civicawareness for retrieving and analyzing textual data with geographical locations and photos as proof. The model has four constituents: data collection, data pre-processing, data analysis, and data visualization. Here, we use the k-means algorithm for clustering and SVM for classification. This system will collect a large number of data that will be used by common people to post the problems they have found. © Grenze Scientific Society, 2023.

关键词： data mining

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