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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nandhakumar, C. MarkElfric, J. Maruthanayagam, S. Naveenkumar, G. Sudharson, T. Department of Computer Science and Engineering M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Natural disasters such as earthquakes, floods are becoming more frequent and severe due to climate change and urban expansion. The ability to quickly detect and assess the severity of such events is necessary requirements for timely disaster response and mitigation. Traditionally, disaster detection relied on isolated data sources like weather reports, seismic sensors, and ground reports. However, the availability of multi-modal data has opened up opportunities for more holistic monitoring systems in disasters. Traditional disaster detection methods remain limited by sheer volumes and complexity, making automated systems a necessity for real-time analysis. This project will explore the concepts of deep learning frameworks for automating classification and analysis of multimodal data for disaster scenarios. DL, deep learning, with powerful tools automatically examines and classifies such enormous heterogeneity in a vast data field. Therefore, from the perspective of DL algorithms and architectures, extraction of meaningful patterns from multimodal data becomes feasible towards rapid and correct disaster detection. © 2025 IEEE.

关键词： Deep learning

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Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-20页

作者： Chen, Peilin Wang, Shiqi Li, Zhu Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science and Electrical Engineering University of Missouri-Kansas City KansasMO United States

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image compression

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Leveraging Machine Learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the IEEE conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Securing Electronic Health Records from Insider Threats in Smart City Healthcare Cloud Using Machine Learning Approach 5

Securing Electronic Health Records from Insider Threats in S...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Ramasami, S. Uma Maheswari, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Erode India Anna University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

In recent years, healthcare in smart cities is considered as significant to create a more resilient and well-informed healthcare ecosystem. The integration of cloud computing in healthcare industry has facilitated the storage and sharing of electronic health records (EHRs), enabling seamless access to medical information. However, this transition to cloud-based systems has introduced new security challenges, particularly the risk of insider threats. Insiders with authorized access to cloud-based EHR systems, such as healthcare professionals and cloud service providers, may exploit vulnerabilities or misuse sensitive patient data, leading to privacy breaches and unauthorized disclosure. This research work presents an innovative approach to securing EHRs from insider threats in the cloud using Gaussian Mixture Model and Classification algorithms. These techniques analyze user behaviour patterns and detect anomalous activities that may indicate insider threats. Here UK hospital electronic patient health record data set is used to conduct the experiments. The proposed approach comprises two key stages. In the initial stage, the unsupervised Gaussian Mixture Model is utilized to find the abnormal patterns in the dataset and label each record in the dataset as normal or anomaly. The second stage involves different supervised classification algorithms namely SVM, KNN, DT, NB and RF are used to classify the new instance. The results demonstrate that the Random Forest (RF) achieves high accuracy in detecting insider threats with an accuracy of 99.97%. The findings of this research contribute to the field of healthcare data security by offering an intelligent and proactive solution for mitigating insider threats to cloud-based EHRs in Smart city environments. © 2024 IEEE.

关键词： Support vector machines

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Illness and Gender Classification of Cattle 1

Illness and Gender Classification of Cattle

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Karki, Rahul Rana, Shubham Jain, Chahak Rawat, Daksh Vats, Satvik Sharma, Vikrant Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350367720

Cattles are one of the most important components of the global agriculture industry as they provide essential resources like meat and dairy products. This is the reason cattle health is very crucial to ensure animal welfare. This paper presents a deep learning model designed to classify cattles based on gender and diagnose illness in an efficient manner. In this paper two different datasets have been used, one for gender classification and the other for illness detection. Through an extensive dataset of cattle images, the model designed using deep learning techniques learns to distinguish between male and female cattle with a high degree of accuracy. Additionally, the model designed here has the capability of detecting signs of illness or distress in cattles based on visual cures, such as changes in posture, skin condition and facial expressions. The model can be proved helpful in timely intervention, therefore improving overall animal welfare economic. Moreover, this research opens doors for the development of similar models in the broader context of livestock management and animal health. © 2024 IEEE.

关键词： Dairy products

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Cryptography machine: Encryption and decryption model for multimedia data 1st

Cryptography machine: Encryption and decryption model for mu...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access is a top priority in today’s digital world. Cryptography is used to ensure secure communication over the internet, including email, instant messaging, and web browsing. Our paper’s primary goal is to develop a cryptography machine capable of encrypting and decrypting different data types, such as plain text, images, audio and videos using the cryptography of the symmetric key. The proposed model will be implemented using Python and Java. Developing the cryptography machine will significantly protect sensitive information and ensure its confidentiality and integrity. The machine’s ability to encrypt and decrypt different data types using symmetrickey cryptography will provide an added layer of security to individuals, organizations, and governments in the digital era. © 2025 the Author(s).

关键词： algorithms Cryptography decryption encryption keys machine

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ADVANCED CHAT APPLICATION INTEGRATING MACHINE LEARNING FOR SENTIMENTAL ANALYSIS

ADVANCED CHAT APPLICATION INTEGRATING MACHINE LEARNING FOR S...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Layek, Santanu Sharma, Gauransh Agarwal, Vihan Chandru Vignesh, C. School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Vellore India School of Computer Science and Engineering Tami Nadu Vellore India

ISBN: (纸本)9798331510299

The "Advanced Chat Application Integrating Machine Learning for Sentimental Analysis" is a progressive interface that utilizes machine learning (ML) algorithm to analyze user's interaction and offer compatibility services with emotional intelligence in real-time. This platform offers the chat user an intelligent map of a comprehensive communication interface with the ability to also complete the human ability in the sense of the differentiation between emotions where it will show empathy based on the context of the user. By the implementation of sentiment analysis feature, the chat application is able to identify the emotions as happy, sad, angry or neutral that people portray when in a conversational mode of text. This is made possible by natural language processing (NLP) encoding using the artificial intelligence models that enjoy the capacity to determine the emotional content of the text messages by virtue of deep learning algorithms which were trained on large data. Therefore, the sentiment detection is able to increase the level of participation, improve on the services offered to patrons, and most importantly, the identification of symptoms of mental disorders in their early stages. © 2024 IEEE.

关键词： Adversarial machine learning

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Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software engineering 46

Breaking Barriers: Investigating the Sense of Belonging Amon...

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46th International Conference on Software engineering: Software engineering Education and Training, ICSE-SEET 2024

作者： Boman, Lina Andersson, Jonatan de Oliveira Neto, Francisco Gomes University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400704987

Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students. © 2024 Copyright held by the owner/author(s).

关键词： Students

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