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Unveiling the invisible struggles: Exploring student perspectives on mental health in universities

IEEE Potentials 2024年第3期43卷 31-35页

作者： Murali, Ritwik Avudaiappan, Veeramanohar Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Coimbatore Coimbatore641112 India Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Coimbatore641112 India

In the current academic environment, the increasing prevalence of stress among students has become a critical concern, significantly affecting both mental health and academic outcomes. Factors such as heavy academic workloads, interpersonal conflicts, financial challenges, and societal pressures contribute to cognitive impairments, procrastination, and declining academic performance. Unaddressed, this stress can escalate into severe mental health conditions, including anxiety, depression, and self-harming tendencies, with marginalized or overwhelmed students being particularly vulnerable. Familial expectations, financial instability, imposter syndrome, fear of missing out, cyberbullying, and unsupportive institutional cultures further intensify these challenges. While mental health support mechanisms exist within institutions, they often remain reactive, addressing issues only after they manifest. To address this growing crisis, a shift toward proactive measures is essential, encompassing counseling, meditation, peer support systems, and engagement in extracurricular activities to foster emotional resilience. A holistic and preventative approach that emphasizes early intervention, open communication, and supportive academic environments is imperative to promote students' well-being and academic success. © 1988-2012 IEEE.

关键词： Students

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Comparison of CNN-based deep learning architectures for rice diseases classification

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Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

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Computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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FARMER: Forecasting Agricultural Results with Machine Learning Evaluation and Regression 11

FARMER: Forecasting Agricultural Results with Machine Learni...

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

作者： Chauhan, Naman Bharathi Mohan, G. Sulochana, V. Kumar, R. Prasanna Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Computer Science and Engineering Chennai India Anna Administrative Staff College Dehradun India

ISBN: (纸本)9798350350357

Agriculture plays an important role, in Indias economy providing support to a portion of the population and contributing significantly to the GDP. It is crucial to predict crop yields in order to make decisions and promote sustainable agricultural practices. To achieve this a research study proposes an approach that utilizes machine learning techniques such as Decision Tree, Support Vector Machine (SVM) Random Forest, Multiple Linear Regression and Partial Least Squares Regression. The study carefully evaluates the performance of these models. Reveals some findings while comparing them to determine the best model. Multiple Linear Regression achieves the accuracy at 81.2% followed by Decision Tree at 83.5% SVM at 92.1% Random Forest at 91.5%. Partial Least Squares Regression at 85.3%. Notably SVM emerges as the performing model demonstrating its potential in improving precision, for predicting agricultural yields. Ultimately an Android application is developed using Kotlin and Android Studio for an user experience. © 2024 IEEE.

关键词： Multiple linear regression

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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

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Computer Modeling in engineering & sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Comprehensive Methods for Secure Medical Image Transfer: Encryption and Standardization Techniques

Comprehensive Methods for Secure Medical Image Transfer: Enc...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Priyadharshini, K. Nandi, Hitesh Parmar, Harsh Dept. of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350376913

The primary goal of the process is to securely transfer medical images. This involves utilizing MRI datasets for processing and applying resizing and filtering techniques such as Conv2D and Gaussian blur to standardize the images. Additionally, an attack model is developed to enhance information security, starting with a PGD attack to access model weights and followed by a DAG attack to increase loss functions. Encryption is then performed using a Chaos-based encryption model to create a chaotic map and the Henon map for image conversion. A randomly generated key encryption method is utilized to generate a key for secure transfer. The encrypted image is displayed, and in the receiver section, the process is reversed for decryption by inputting the key generated in the encryption model. Finally, the original medical image is retrieved along with parameters such as MSE, MAE, RMSE, and F2 score to assess the accuracy of the recovered image and ensure its correctness. © 2024 IEEE.

关键词： Cryptography

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Unveiling the Digital Shadows: A Comprehensive Exploration of Network Forensics Methodologies 2

Unveiling the Digital Shadows: A Comprehensive Exploration o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Nandini, Dhanalakota Sai Jyothika Lanitha, B. Azath, H. Ganeshan, R. Ahmed, Sajjad VIT Bhopal University Kothrikalan School of Computing Science and Engineering Sehore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India VIT Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331534950

The Internet has experienced significant growth in attacks that affect network security features such as confidentiality, integrity, and availability. Network security and network forensics work together to ensure system integrity, availability, and confidentiality. Network security measures prevent unau-theorized access, attacks, and breaches, while network forensics investigates and analyses the aftermath of security incidents. Network forensics refers to the process of collecting, analyzing, and preserving digital evidence from network activity in order to investigate and respond to security incidents, breaches, or other malicious activities. It involves reconstructing the events to determine attack methods, compromised data, and potential perpetrators. It lies at the intersection of digital forensics, incident response and network security. This paper is going to analyse the methodologies which are used in network forensics and application of new technologies in each network forensics methodology through ten figures. This paper critically examines network forensics methodologies, focusing on the evolution of these methods and the integration of emerging technologies. It assesses new strategies and discusses inherent issues and limitations of existing techniques, emphasizing the need for cutting-edge techniques to enhance the effectiveness of network forensic processes. © 2024 IEEE.

关键词： Network intrusion

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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