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1st ACM Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Indra, R. Parthasarathy, P.D. Ambasana, Jatin Satavlekar, Spruha Department of Information Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Computer Science and Information Systems Bits Pilani KK Birla Goa Campus Goa India Centre for Educational Technology Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798400705984

Machine learning (ML) course for undergraduates face challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters certain limitation in participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other courses. To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon that combines project-based learning with Hackathon elements. Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the Hackathon design and implementation, highlights successes and areas for improvement making it feasible for replication by interested computing educators. © 2024 ACM.

关键词： Adversarial machine learning

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Triangular Mesh and Neural Network for Object Search Based Cluster Centre Descriptor

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SN computer science 2024年第8期5卷 1-16页

作者： Hosur, Ravi Mahendrakar, Pavan Hiremath, Ananda Noola, Daneshwari Department of Computer Science and Engineering BMS Institute of Technology and Management Doddaballapur Main Road Avalahalli Yalahanka Karnataka Bengaluru India Department of Computer Science and Engineering BLDEA’s V.P. Dr. P.G. Halakatti College Engineering and Technology Karnataka Vijayapur India Department of Information Science and Engineering BLDEA’s V.P. Dr. P.G. Halakatti College Engineering and Technology Karnataka Vijayapur India

Since 3D models and printing have become more and more widespread, 3D object recognition has gained appeal. It might be challenging to locate the needed object in a large database of 3D objects of different kinds. Several methods have been put forth in the past to effectively identify and detect 3D objects based on their shape data. These techniques describe 3D objects in a high-dimensional feature plane either by using geometric shape features or by creating a 3D shape model from a 2D shape model. One of the disadvantages of 3D descriptors is that their high memory requirements make recognition speed a major difficulty. We provide a unique 3D object search method based on 3D object identification to get around the problem. The process reads the 3D vertices from a CAD file, samples the triangular meshes from below, locates the center of each triangle, and then projects the points onto the front and top views of the YZ and XY planes, respectively, in two dimensions. Ten clusters are formed from the points in each plane, and the feature descriptor for the three-dimensional object is derived from the cluster centers. The shape is indexed using this description. In order to extract the descriptor from CAD files, the system looks for objects in a designated directory. A feed-forward neural network is then used to classify the descriptor. We demonstrate that the proposed method outperforms the existing shape descriptor-based methods in terms of speed 139 ms, classification accuracy of 98.3%, and search time. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Centre discriptor Content based neural network Triangular mesh

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Methods for Detecting Android Malware: Employing Mobile Devices to Improve Procedures for Inquiry-Based Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 103-114页

作者： Yelisetti, Sandeep Suresh, P. Jha, Suresh Kumar Arora, Neha Anand, Ketan Sailaja, Vedala Naga Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Thandalam Chennai India Manipal University Rajasthan Jaipur India School of Information Science Presidency University Karnataka Bengaluru India Telangana Hyderabad India Dept of MBA KLEF Deemed to be University Green Fields Andhra Pradesh Vaddeswaram India

The operating system (OS) of a computer controls both its hardware and software. It handles necessary functions including input and output processing, file and memory management, and peripheral device management, including disc drives and printers. Programs created for particular purposes are referred to as application software. These programs, which are frequently open source and freely accessible, contribute to the growing number of downloads. This paper discusses the basics of Android malware, its evolution, and malware analysis tools and techniques. Providing the research gaps and giving a review of the literature on Android malware detection using machine learning and deep learning are its main objectives. It offers the knowledge gathered from the literature as well as potential avenues for future research, which may aid in the development of reliable and precise methods for classifying Android malware. This paper conducts a systematic and comprehensive assessment of the methods and tools utilized for the analysis, classification, and detection of malicious Android apps. Several research gaps are indicated based on the thorough literature evaluation. Additionally, the report offers insights on future research paths that may aid academics in developing novel and reliable methods for identifying and categorizing Android malware. © 2025 by the authors of this article.

关键词： Android malware

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Comprehensive Earth System Analysis for Advancing Community Resilience: Integrating Remote Sensing with Hybrid GAN-RNN Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 788-803页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, Saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. By combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AI-driven solutions Climate resilience Data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing Synthetic data Temporal pattern recognition

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Deployment of Co-operative Farming Ecosystems Using Blockchain 22nd

Deployment of Co-operative Farming Ecosystems Using Blockcha...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on information Assurance and Security, IAS 2022

作者： Mahapatra, Aishwarya Gupta, Pranav Swarnkar, Latika Gupta, Deeya Kar, Jayaprakash Centre for Cryptography Cyber Security and Digital Forensics Department of Computer Science & Engineering Department of Communication & Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9783031274084

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their ***, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Book Genre Classification System Using Machine Learning Approach: A Survey 5th

Book Genre Classification System Using Machine Learning Appr...

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Sethy, Abhisek Rout, Ajit Kumar Gunda, Chandra Routhu, Sai Kiran Kallepalli, Srinu Garbhapu, Geetha Department of Information Technology GMR Institute of Technology Srikakulam Rajam India Department of Computer Science & Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789811986680

The amount of complicated texts and documents that require a deeper understanding of machine learning techniques has expanded rapidly in recent decades. Several machine learning approaches have shown exceptional results in NLP. Complex models and non-linear data correlations help these learning systems function well. It is difficult for academics to discover appropriate text classification structures, designs, and procedures. Manually reading and classifying a book's category would be tedious. It takes a long time for a language beginner to absorb the complete text in order to discern its genre. Natural Language Processing (NLP), which is commonly used nowadays, can help overcome this issue. It includes text classification and summarization. The book synopsis is input to the Machine Learning algorithms, which output the genre. This review includes text feature extraction, dimensionality reduction, algorithms, and evaluation. Finally, the limitations of each strategy are assessed, as well as their practicality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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Implementation of Convolutional Neural Network with EfficientNet-B0 Architecture for Brain Tumor Classification 8

Implementation of Convolutional Neural Network with Efficien...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Hadi, Vinsensius Haryo Bhaskoro Mutiara, Achmad Benny Refianti, Rina Gunadarma University Informatics Engineering Depok Indonesia Gunadarma University Computer Science and Information Technology Depok Indonesia

ISBN: (纸本)9798350342604

Brain tumor is a type of cancer that is difficult to detect in the early stages and can be a fatal disease. Early detection of brain tumors is crucial to increase the chances of successful treatment. The use of imaging technologies such as CT-Scan and MRI has helped in detecting brain tumors, but accurate and fast diagnosis remains a challenge. In this study, the Convolutional Neural Network (CNN) method was used as an alternative method to detect brain tumors faster and more accurately. Some CNN architectures used include VGGNet, InceptionResNet, and the latest architecture called EfficientNet-B0. This research aims to compare the performance of EfficientNet-B0 with other CNN architectures in classifying brain tumors on MRI images. The results of this study are expected to provide an understanding of the effectiveness of using EfficientNet-B0 compared to other architectures in classifying brain tumors on MRI images. The dataset used consists of 3264 images with glioma, normal, pituitary, and meningioma classes. Testing was conducted with several scenarios for epochs and testing against optimizers such as Adam and RMSProp. The results from testing the model using confusion matrix showed that in experiments with an epoch scenario of 25 and Optimizer obtained an average precision value reaching 98%, average recall reaching 98%, and average F1-Score reaching 98%. The implementation of the best-designed model in the form of a system successfully predicted types of glioma, normal, pituitary, and meningioma tumors. Additionally, the classification model will be implemented into a website using Python programming language along with Flask framework. © 2023 IEEE.

关键词： Python

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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

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International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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Threshold Based Real-Time Landslide Prediction System Using Low-Cost Sensor Networks 3

Threshold Based Real-Time Landslide Prediction System Using ...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Kaushal, Arush Sehgal, Vivek Kumar Jaypee University of Information Technology Department of Computer Science and Engineering H.P. Waknaghat Solan India

ISBN: (纸本)9798350302288

Landslides pose a recurring threat in the Himalayan region, leading to devastating consequences in terms of human casualties and property damage. This research introduces a groundbreaking approach to real-time landslide monitoring and prediction, leveraging an affordable sensor network. The system employs a network of wireless sensors strategically placed to continuously monitor environmental conditions and gather pertinent data. This data is then transmitted to a cloud server via a WiFi module, where it is integrated into the PowerBI platform for visualization and analysis. To predict landslides in real-time, the collected data is fed into a machine learning model. The performance of three algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Decision Tree (DT), was evaluated using the acquired data. Among these algorithms, SVM demonstrated exceptional results, exhibiting low values for key error metrics such as Mean Squared Error (MSE=O.067), Mean Absolute Error (MAE=O.246), and Root Mean Squared Error (RMSE=0.204). Threshold values are determined based on the training model's output, enabling the generation of alerts and warnings at different levels. This innovative system significantly enhances preparedness and response strategies in landslide-prone areas, equipping stakeholders with timely information to effectively mitigate the impact on lives and infrastructure. © 2023 IEEE.

关键词： Decision trees

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