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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Amish Priyadarshi, Prakhar Panwar, Arvind Dubey, Arun Kumar Jain, Achin Bharati Vidyapeeth College of Engineering New Delhi India School of computing science engineering Galgotias University India

ISBN: (纸本)9798350379716

Detecting and classifying brain tumors is essential in medical imaging. Accurate and early diagnosis significantly impacts treatment outcomes. Deep learning (DL) models provide automated and highly accurate diagnostic tools. In this paper, we have used publicly available MRI brain tumor dataset from Kaggle. It evaluates the performance of various advanced DL architectures to classify brain tumors. We used several pre-trained deep learning models, including Inception ResNet V2, Inception V3, ResNet 50, VGG 16, Xception, and VGG 19. Transfer learning was employed to fine-tune these models on the Kaggle MRI brain tumor dataset. Each model was trained and validated to assess its classification accuracy. Transfer learning allowed the models to utilize pre-existing knowledge, enhancing their performance in this medical imaging task. The validation accuracies achieved were as follows: Inception ResNet V2 at 62.39%, Inception V3 at 74.56%, ResNet 50 at 81.34%, VGG 16 at 89.83%, Xception at 70.67%, and VGG 19 at 92.37%. Among these, VGG 19 showed the highest accuracy. It demonstrated superior capability in distinguishing between different types of brain tumors, with an improvement of 6.93% over other models. © 2024 IEEE.

关键词： Contrastive Learning

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From FOMO to a Legitimate Wellbeing Concern: The Evolution and Impact of Disconnectivity Anxiety According to the Current Research 29

From FOMO to a Legitimate Wellbeing Concern: The Evolution a...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing prevalence of smartphones and social media has sparked growing concerns about anxiety related to digital disconnection, particularly in the context of problematic smartphone use and dependence (PSUD). Fear of missing out (FOMO) is a widely recognised form of anxiety associated with frequent smartphone use, especially on social media apps, and it has been primarily observed among younger adults. However, beyond FOMO, other technological concerns, such as nomophobia (no-mobile-phone phobia) and technoference (the interference of technology in interpersonal relationships), have also become more prevalent, highlighting the broader impact of smartphone use on individuals' mental wellbeing. My ongoing PhD research into the identification and management of PSUD aimed to delve deeper into understanding the complexities of PSUD and its effects on individuals' lives. FOMO is one of the critical aspects examined within the broader framework of PSUD, as it can have significant implications for mental health and wellbeing. It also explored how FOMO may arise both from PSUD and effectual smartphone use, where individuals may feel concerned about missing important meetings, notifications, calls, or other responsibilities related to their social and professional roles. The multifaceted cause of FOMO was one of the focal points of my investigation, encompassing factors such as the need for connectedness for various reasons and constant exposure to media. I have investigated how FOMO can lead to feelings of inadequacy, loneliness, and anxiety, impacting individuals emotionally and socially. This could include further examining how FOMO may negatively impact self-esteem, increase stress, and contribute to depression and its influence on developing unhealthy coping mechanisms and impulsive behaviours. Addressing FOMO and digital disconnection anxiety within the context of PSUD requires a holistic approach that involves self-care, mindfulness practices, and boundary setti

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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Computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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Quality Grading Methods for Greenhouse Grown Crops Using Computer Vision and Machine Learning - A Review 50

Quality Grading Methods for Greenhouse Grown Crops Using Com...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Pallewatta, Pandula Karunanayaka, Kasun Arachchi, Samantha Mathara Halloluwa, Thilina Seneviratne, Gihan University of Colombo School of Computing Sri Lanka School of Electrical Engineering and Computer Science The University of Queensland Australia

ISBN: (纸本)9781665464543

This review paper discusses the usage of Computer Vision (CV) and Machine Learning (ML) in greenhouse environments for crop grading. It focuses on the progress of autonomous quality gardening using crop image acquisition, preprocessing, and grading over the past five years. The review focused on greenhouse crops: apple, mango, bell pepper, papaya, tomatoes, strawberry, carrot, and chilli. The study focuses on noise reduction, image segmentation, and feature extraction techniques. To improve the assessment and classification of crop quality. Classification using ML algorithms, such as Random Forest, Support Vector Machines (SVM), and K-nearest Neighbours (KNN), are discussed. The addition of AI-driven approaches in agricultural practices is prioritized to enhance crop quality, minimize environmental impact, and satisfy market standards. This review is a comprehensive resource for researchers and practitioners interested in utilizing AI technologies to achieve sustainable and efficient crop production. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced U-Net++ Architecture for Precise Brain Tumor Segmentation in MRI Images: A Robust Solution for Medical Image Analysis

Advanced U-Net++ Architecture for Precise Brain Tumor Segmen...

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2024 IEEE International Conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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Topology Construction for Max-Min Rate Optimization in Heterogeneous UAVs Networks

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IEEE Internet of Things Journal 2025年第12期12卷 20203-20214页

作者： Wu, Kefeng Chin, Kwan-Wu Soh, Sieteng Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Curtin University School of Electrical Engineering Computing and Mathematical Sciences PerthWA6102 Australia

This paper considers a topology construction problem involving a fixed-wing Unmanned Aerial Vehicle (UAV), a set of quad-rotor UAVs and mobile ground users. The constructed topology must maximize the max-min flow rate of ground users over a given planning horizon. To this end, we outline a Mixed Integer Linear Program (MILP) that jointly optimizes the trajectory of the fixed-wing UAV, placement of quad-rotor UAVs, and routing of traffic from each source-destination ground user pair. Solving the MILP is challenging because it requires an exhaustive collection of topologies. To this end, this paper outlines a solution called Rollout to determine the network topology in each time slot of a given planning horizon iteratively. The main idea of Rollout is to generate a sequence of future decisions using a heuristic, where a decision corresponds to the placement of quad-rotor UAVs. Further, each sequence of decisions has a cost-to-go value. Rollout then selects the sequence with the highest cost-to-go value. The results show that the max-min flow rate achieved by Rollout is on average 81% that of MILP. © 2014 IEEE.

关键词： Antenna grounds

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Explaining Parkinson's Disease Detection by Ensemble Classifiers with Local Interpretable Features 8th

Explaining Parkinson's Disease Detection by Ensemble Classif...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Karn, Alok Kumar Dixit, Aniket Pati, Peeta Basa Mahalingam, Anand Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

Parkinson's disease (PD) is a neurodegenerative condition and a progressive nervous system disorder condition, marked by its characteristic symptoms such as tremors, muscle stiffness, and slow, imprecise body movements. Typically affecting the middle-aged and elderly, the most crucial challenge in the treatment of PD is posed in the form of its early diagnosis. While there is no cure for the disease, early intervention is found to have alleviated the symptoms and to have also improved the overall quality of life, potentially slowing down the disease’s progression. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), almost every industry seems to be benefiting from its incorporation and the field of biomedical sciences is no different. AI-ML techniques have seemed to be specifically beneficial in classification-based biomedical problems. This research is built upon this very same idea. The application of 14 ML algorithms is studied on a dataset that consists of vocal characteristics of several individuals, both the healthy and the PD patients, to classify based on a patient’s speech whether or not, they have Parkinson’s. To improve the overall performance of the algorithms, SMOTE and PCA pre-processing techniques have also been applied to deal with the dataset’s shortcomings. The study observes that Random Forest, Gaussian Process, Extra Trees, and CatBoost are the 4 best performing algorithms for the task, providing an accuracy of 96.61%. The paper concludes with the deployment of an Explainable AI (XAI) LIME module on the best-performing algorithms to provide transparency and interpretability to the decision-making process of these AI systems. © IFIP International Federation for Information Processing 2025.

关键词： Neurodegenerative diseases

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