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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Swasha, K. Gracia, S. Steffie Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Students frequently experience issues while taking notes, which interferes with their ability to study. There are many reasons students miss to take notes in college. Without notes, they might miss on important information and it would be really hard for the students during their exam times. Students will have a place to congregate and engage in collaborative learning through our application. It is possible for students to their materials (both written notes and documents in PPT, PDF, DOC, etc. format) for free access by other students in any branch or semester. Each user will log in to the application using their individual account. This application will also help the students to earn money. Students who wish to earn money while studying can lock their notes and the other students who want to download the notes have to pay the uploader. This way the application will benefit students to earn part time and also encourage students to take notes in class. There are few existing models for the said application but users face many difficulties like access to notes only after payment, does not accept hand written notes, must upload notes regularly, allows upload only for a particular stream. The proposed method provide solutions like students from different streams can upload their notes, notes can be downloaded for free or paid version based on how the uploader has set the download option. The other important feature is that Student Performance Analysis is done using Random Forest Regressor. The future work of this application is to enable student credit other students for their work by including credit system which will motivate the students to do a better job. This application is intended to be an Educate and Share application. © 2023 IEEE.

关键词： Students

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Generating Realistic X-ray Images Using GANs 26

Generating Realistic X-ray Images Using GANs

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Muthaki, Tahsina Masuk, Safwan Ibne Ahsanullah University of Science & Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Modern developments in the medical industry have been significantly accelerated by the promising advancements of Generative Adversarial Networks (GANs). This study introduces an intriguing exploration into the utilization of Deep Convolutional Generative Adversarial Networks (DCGANs). Using GANs' inherent capabilities, our objective is to build high-fidelity pictures with detailed anatomical features and qualities similar to actual X-ray images. The proposed methodology of this work proves effective in addressing the challenges posed by limited medical imaging data. It generates diverse and expansive datasets, which can enhance machine learning models in medical research and diagnostics. The study presents a novel approach to create synthetic X-ray images that closely resemble original CXR images, with the potential to significantly contribute to medical imaging. Consequently, our research not only advances the application of GANs in medical imaging but also holds promise in shaping the future landscape of healthcare technology. © 2023 IEEE.

关键词： Cardiomegaly COVID-19 DCGAN Pneumonia Tuberculosis X-Ray

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A Novel Detection on Wheat Disease through CL and RGB Filters by LDA and QDA 2

A Novel Detection on Wheat Disease through CL and RGB Filter...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Rajesh Kanna, R. Ulagamuthalvi, V. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

Wheat is essential everywhere. Wheat leaf diseases hinder growth. Wheat leaf disease identification is crucial to wheat quality and agriculture. This work presents an integrated machine learning strategy to improve wheat leaf disease identification: Colour Layout Filter with Linear Discriminant Analysis, Colour Layout Filter with Quadratic Discriminant Analysis, RGB Filter with Linear Discriminant Analysis and RGB Filter with Quadratic Discriminant Analysis can identify damaged wheat leaves. The agricultural autonomous leaf infection detection system uses images, processing, feature extraction, selection, and learning. This technology helps farmers quickly and reliably diagnose plant illnesses. Automatic leaf disease detection speeds crop diagnosis. This study's Linear Discriminant Analysis Color Layout Filter classifies wheat diseases well. LDA-CLF is most accurate at 88.33%. QDA-RGBF has 80% accuracy. CLF with LDA 0.88 is ideal. RGBF QDA accuracy is 0.80, poor. LDA-CLF recalls 0.88. RGBF with QDA recall is 0. S0, low. With 0.66 kappa, CLF and LDA lead. RGBF with QDA has lowest kappa (0.49). Our best model is CLF's0.88 LDA F-Measure. QDA-enhanced RGBF has 0.80 F-Measure. LDA-CLF has the highest MCC at 0.66. RGBF QDA MCC lowest is 0.5. 0.93 CLF-LDA ROC. RGBF's LDA-based ROC is 0. S4. RGBF QDA models have the highest PRC (0.92). RGBF-LDA has the lowest PRC (0.78). Linear Discriminant Analysis-based Color Layout Filters outperformed other models in this study. © 2023 IEEE.

关键词： Color

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A survey on graph neural network-based next POI recommendation for smart cities

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Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

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Data Transformation, Modelling and Prediction of Customer Churn using Deep Learning 14

Data Transformation, Modelling and Prediction of Customer Ch...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Menaka, S. Ram, B. Raghu Gowtham, N.V.S. Reddy, G.V. Madhu SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

Client prediction of churn is a difficult field of study that adds to client retention strategies. In a saturated and competitive market, telecommunications companies must rely on a viable churn customers prediction model to retain consumers. Previous research has focused on predicting customer attrition in the present moment or next month, when telecom businesses have not got enough time to create and implement churn control solutions. The predictive performance of present machine learning algorithms, which are frequently used by churn communities, appears to be at a stalemate, owing in part to the algorithms' weak feature extraction capacity. As a result, this research proposes a unique technique, a mix of neural networks with self- attention augmentation, to increase the efficiency of features filtering and feature extraction, hence enhancing model prediction performance. This study is being undertaken in the context of forecasting customer churn (CCP) in the telecommunications industry (TCI), wherein customer attrition is a typical occurrence. For the CCP problem, we developed a unique solution that combines data transformation approaches with machine learning models. We ran our tests on publicly accessible TCI datasets and evaluated performance using widely used assessment metrics (e.g., AUC, accuracy, recall, and F-measure). In this work, we used extensive comparisons to verify the impact of modification strategies. © 2023 IEEE.

关键词： Telecommunication industry

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Extending Scratch Framework to Improve the Analytical Skills of Undergraduate Students 24

Extending Scratch Framework to Improve the Analytical Skills...

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10th International Conference on computer technology Applications, ICCTA 2024

作者： Akbar, Ubaid Ul Akbar, Saeed Ali, Mumtaz Ullah, Rahmat Khan, Rizwan Lopes, Ivandro Ortet Department of Computer Science City University of Science and Information Technology Peshawar Pakistan School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science and Electronic Engineering University of Essex United Kingdom School of Cyber Science & Engineering Huazhong University of Science & Technology Wuhan China

ISBN: (纸本)9798400716386

Scratch is an innovative and the most popular block-based Visual Programming Language designed for beginners to learn programming effectively. However, it lacks the capacity to allow users to learn and solve larger real-world problems such as the Travelling Salesman Problem (TSP). Hence, there is a need for an accessible and effective tool to assist beginners and novice programmers in learning and tackling the TSP. This paper introduces ScratchTSP, an extension of the Scratch programming Framework, specifically designed for the TSP. It offers an opportunity for beginners and novice programmers to grasp the TSP and the various algorithms used to solve it. For evaluation, we conduct a survey among undergraduate students to assess the usability and usefulness of the proposed ScratchTSP extension. Survey findings indicate that ScratchTSP offers a user-friendly and effective tool for beginners or novice programmers, aiding them in learning the TSP while developing their critical thinking and problem-solving skills. © 2024 ACM.

关键词： Students

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security 2

Innovative Data Encryption Techniques using AI for Wireless ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Sustainable Development of Employability and Skill Development in Outcome-based Education: A Machine Learning Approach 2

Sustainable Development of Employability and Skill Developme...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Bora, Mousoomi Baruah, Rupam Assam Science and Technology University Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9781665491990

The contribution of technological enhancement has played a vital role in strengthening the country's economy. The Higher Education lnstitutions (HEI) are providing placement assistance to the students by employing various measures as a part of their curriculum. A few of the attempts that HEIs have adopted to enhance soft skills as well as hard skills of the students are, course structure modification, student-mentoring, industry-academia collaboration etc. In spite of all the noble efforts, many unemployed engineers are witnessed in the society. Therefore, this research aims to develop an effective and accurate framework that would be highly beneficial for both students as well as teachers in HEIs. A Semi-Supervised-Sequence-Prediction (SSSP) machine learning framework has been designed to predict students' academic achievement towards the completion of a four-year degree course in an outcome-based education system. A time series analysis of students' data has been conducted to analyze the progression of final cumulative GPA and employability prospect in sequential manner. The results show the effectiveness of the proposed framework in predicting employability of students along with the areas where students need improvement in order to be employable. This research provides an inclusive roadmap, empowering the use of machine learning for employability and skill development in outcome-based education. © 2023 IEEE.

关键词： Students

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Forest Cover Type Classification Using Machine Learning Algorithms 2

Forest Cover Type Classification Using Machine Learning Algo...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Suhail, Shaik Javeed Refonaa, J. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347456

It is important to understand the different types of forest cover because forests are critical for decreasing air pollution, controlling greenhouse gas emissions, and preserving ecological equilibrium. So, it becomes very important to understand the types of trees present in different parts of the forest. So, using machine learning techniques, a structure is developed to entirely automate this procedure. A variety of classification algorithms were used to classify different forest cover types based on cartographic features. Supervised machine learning algorithms were used. Classification algorithms used in this study were Decision Tree Classifier, Multinomial Logistic Regression, k-Nearest Neighbors Classifier, and Random Forest Classifier. Different performance criteria were performed to measure each classifier's performance. The Random Forest classifier outperforms the other three classifiers in this study. It helps the users classify the cover type easily without having to deal with all that data. In the end, it is integrated with the Flask framework. With the help of this classification, further research can be carried out regarding the inhabitants of forests, deforestation, forest fires, and many other concerns. © 2023 IEEE.

关键词： Deforestation

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Spider Monkey Based K-Means Dynamic Collaborative Filtering for Movie Recommendation Systems 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Devi Priya, R. Reddy, Boggala Thulasi Hariharan, P. Department of Computer Science and Engineering Centre for IoT and Artificial Intelligence KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Building Automation Recommender Systems (BARSs) can keep constructing proprietors’ cash by means of lowering power intake whilst concurrently retaining occupant comfort. There are algorithms that optimize this exchange off, including detecting which home equipment have become the one without requiring costly popularity detectors to be connected to every appliance. However, efficient methods have to be identified which are best suited for specific constructing. Recommender structures were used extraordinarily, academically and commercially. The guidelines are generated by using methods which are capable of providing applicable thrilling gadgets to customers. Several techniques were proposed for offering customers with guidelines for the use of their score records. Most of those techniques are afflicted by new person problem (cold-begin) which is the preliminary loss of gadgets reviews. In this paper, demographic statistics are used to offer guidelines rather than the use of score records to keep away from cold-begin problem. Spider Monkey based K-Means Dynamic Collaborative Filtering is proposed for movie recommendations and the results are satisfactory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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