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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350307757

This article explores the diverse impact of the Internet of Things (IoT) on education. This research investigation examines the possible uses of the IoT in several educational domains, with a particular focus on the significant impact of IoT on student engagement, learning methodologies, and administrative efficacy. While explaining the considerable advantages, this discourse also acknowledges the significant obstacles pertaining to integration, security, and privacy. The study finishes by presenting a forward-looking model for smart campuses, providing a holistic viewpoint on the IoT impact on transforming education. The article examines the impact of the IoT on educational practices and emphasizes the significance of institutional adaptation to effectively incorporate this technological paradigm shift. The article presents an elaborate framework for the prospective development of intelligent campuses and higher education institutions. This article offers a comprehensive guide for effectively using the whole capabilities of the IoT within the realm of education. © 2024 IEEE.

关键词： Internet of things

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A nonlinear recurrent encoders for early detection of strep throat infection to prevent acute rheumatic fever

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2201-2213页

作者： Kumar, K. Antony Belinda, M. J. Carmel Mary Kumar, V. Dhilip Rajan, Jerlin Francy Arif, Muhammad Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Medical Surgical Nursing Dr. Jeyashekaran Medical Trust Nagercoil India Superior University Lahore Pakistan

Rheumatic Fever or Acute Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD) are commonly suffered by people of all ages around the world. RF usually begins with strep throat infections, skin problems, joint pain etc. Clinical tests and medical informatics are useful to save patients from Streptococcus Group-A bacteria using medical treatments and detection mechanism. Clinical data based statistical analysis approaches assist medical diagnosis model in detecting diseases based on standard decision. Standard decision making frameworks are effective when using these approaches. Nevertheless, existing mechanisms cannot detect or predict the incidence of strep throat infection at an earlier stage based on unclear datasets or complex datasets. The recent computational healthcare informatics systems use Machine Learning (ML) and Deep Learning (DL) techniques, but they are not sufficient in terms of their field integration. The proposed work implements a nonlinear recurrent auto encoder using complex data evaluation procedures to accurately detect and predict the strep throat infections accurately. In this regard, the proposed model builds on the technical features of Denoised Variational Stacked Auto Encoders (DVSE), nonlinear regression computations and Long Shot Term Memory (LSTM) to perform recurrent data analysis practices. Compared with the existing techniques such as Blood Serum Content Analysis model (BSCA), Smartphone based Strep Throat Detection (SSTD) approach and Logistic Regression Based RF Detection approach (LRRF), the multi-level medical data evaluation (throat images and blood samples) provides 10–15% more accuracy for strep throat detection. As shown in the implementation section, the proposed nonlinear recurrent system has the potential to detect strep throat infections early. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diagnosis

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review 15

Machine Learning Classification of Shoulder Pain Pathologies...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Garima Bhagat, Khushi Chaudhari, Vedant Navaneeth Krishnan, E. Bhadauria, Mansi Chauhan Chouksey, Yash Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350370249

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field. © 2024 IEEE.

关键词： Patient treatment

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

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

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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Bluetooth Low Energy-Based Novel Power Efficient Buffalo Calving Detection Solution

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 6-10页

作者： Raina, Radhika Jeet Singh, Kamal Kumar, Suman Jain, Sarthak Indian Institute of Technology Ropar Department of Electrical Engineering Rupnagar140001 India Guru Gobind Singh Indraprastha University Computer Science and Engineering New Delhi110078 India

This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is designed for buffalo comfort and can be conveniently attached to the tail. The device wirelessly transmits data to a gateway using Bluetooth Low Energy (BLE). This functionality becomes particularly crucial when the tail movement increases in the last 12 hrs before calving and is regarded as a key behavioral indicator for predicting the onset of labor. Moreover, when an accelerometer is tied to the buffalo's tail, the Z axis, which represents the vertical axis perpendicular to ground is anticipated to show the most notable deflections during this period as discussed in the literature. Thus, to conserve power, data is only transmitted when significant tail movement is detected, typically 12 hrs before calving, i.e., when Mean Z >-3 m/s2. This approach reduces the device's power consumption, extending its battery life to more than 6.08 years (approx.) using 620 mAh / 3V battery. © 2019 IEEE.

关键词： Accelerometers

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Enhancing Offensive Bengali Social Media Meme Detection: A Weighted Ensemble Architecture for Predicting Type and Target Classes 26

Enhancing Offensive Bengali Social Media Meme Detection: A W...

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

作者： Islam, Md. Maruful Roy, Nidita Sheila, Tania Sultana Ibrahim Khan, Muhammad Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chattagram4349 Bangladesh Bangladesh Army International University of Science & Technology Department of Computer Science and Engineering Cumilla3501 Bangladesh

ISBN: (纸本)9798350359015

This paper investigates the classification of offensive memes using a weighted ensemble approach of multimodal models that integrates visual and textual information. A meticulously curated dataset, Multi-modal Offensive Memes Bengali Dataset (MoMBD), containing 3662 offensive Bengali memes, is created to facilitate work in this domain. A three-level hierarchical classification system is followed to accurately categorize the fine-grained categorization of offensive memes. In this paper, BERT, as text classifier, is combined with transfer learning model, as image classifier, to create a multimodal unit. Then, a weighted ensemble is performed on the three best-performing multimodal models. The proposed weighted ensemble technique is effective in achieving high accuracy and f1-scores for both coarse-grained and fine-grained classifications. The proposed method achieves an impressive weighted f1-score of 71.84% in fine-grained classification. Overall, this research contributes valuable insights and techniques for detecting offensive Bengali memes, type and their target. © 2023 IEEE.

关键词： coarse-grained fine-grained Low-resource languages Multimodal Offensive memes Three level dataset weighted ensemble

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Deformation mechanism of Cu-Al-Ni shape memory alloys fabricated via laser powder b e d fusion:Tension-compression asymmetry

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Journal of Materials science & technology 2023年第36期167卷 14-26页

作者： Yankun Zhang Lianyong Xu Lei Zhao Danyang Lin Minqian Liu Wei Chen Yongdian Han School of Materials Science and Engineering Tianjin UniversityTianjin 300350China Tianjin Key Laboratory of Advanced Joining Technology Tianjin 300350China State Key Laboratory of Engines Tianjin UniversityTianjin 300350China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Introducing the unique advantage of additive manufacturing technology into copper-based shape memory alloys(SMAs)to fabricate high-performance alloys has garnered great attention in recent years,but the intrinsic relationships between microstructure and mechanical properties need to be further *** this paper,the microstructural evolution of ternary CuAlNi SMAs fabricated by laser powder bed fusion(LPBF)under the tensile-compressive loading was investigated to determine the underlying mechanism of tension-compression asymmetry,that is,excellent compressive but poor tensile *** characterization of the different deformation stages revealed the numerous activated deformation mech-anism on the 18R martensite matrix.A twin-related transformation dominated the main plastic defor-mation process due to lower stacking faults energy and high-density pre-existing planer defects in the CuAlNi *** twinning nucleated at prior austenite boundaries and developed into parallel and network structures inside the parent grain of different *** addition,the preferred orientation in different stages,the stress-inducedγphase transformation,and the interaction between dislocations and stacking faults are *** results not only provide significant insights to understand the detwinning and deformation twinning process of SMAs but also establish the essential framework of mi-crostructure and mechanical properties of Cu-based SMAs fabricated by LPBF.

关键词： Shape memory alloys Cu-based Laser powder bed fusion Microstructural evolution Deformation mechanism

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Malware Detection in IoT Devices Using Machine Learning: A Review

Malware Detection in IoT Devices Using Machine Learning: A R...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Singh, Deepika Khurana, Shiraz Sharda School Of Engineering And Technology Sharda University Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350306446

A thorough summary of recent studies on malware detection in Internet of Things environments can be found in the literature review section. The necessity for complex and scalable detection techniques is emphasized by examining various approaches, from signature-based to anomaly detection approaches. Furthermore, the discussion comprises the application of supervised, unsupervised, and semi-supervised machine learning algorithms to mitigate IoT malware threats. While unsupervised techniques like clustering algorithms show promise in detecting new threats, supervised learning techniques like support vector machines (SVM) and random forests (RF) effectively identify known malware patterns. Furthermore, semi-supervised learning techniques present a viable way to improve detection abilities by utilizing compactly labeled data. © 2024 IEEE.

关键词： Internet of things

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Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins 17

Towards Secure AI-driven Industrial Metaverse with NFT Digit...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Prakash, Ravi Thomas, Tony Kerala University of Digital Sciences Innovation and Technology School of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331531195

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse. © 2025 IEEE.

关键词： Smart contract

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Taming Breast Cancer Diagnosis Through Transfer Learning: A Binary Classification Framework for Enhanced Disease Detection 4

Taming Breast Cancer Diagnosis Through Transfer Learning: A ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Shandilya, Gunjan Anand, Vatsala Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science Punjab India

ISBN: (纸本)9798350387490

Among the deadliest types of disease is cancer. It is also the most prevalent cancer in women, and if it is not correctly identified, it can even be fatal. Although medical technology has advanced significantly over the years, breast cancer detection remains a challenge that can only be solved through biopsy. By examining histological pictures seen under a microscope, pathologists can identify cancer. Visual inspection is an important part of cancer inspection;it takes time, competence, and a great deal of attention to detail. A quicker and more effective method of identifying breast cancer is therefore required. Numerous studies are being conducted to develop an effective partially or computer-monitored diagnostic system due to developments in deep learning and image analysis. To differentiate between benign and malignant forms of breast cancer, this study uses histopathological scans and convolutional neural networks in combination with transfer learning, a sophisticated kind of image-based machine learning. A large-scale multi-cancer dataset is used in the study's analysis. A refined VGG16 model that has been pre-trained for the categorization of breast cell images is presented in the article, exhibiting outstanding outcomes with an overall accuracy of 97.89 %, precision of 97 % and recall of 98 %. The incorporation of this model into healthcare systems has the potential to enable timely and precise diagnosis of breast cancer. © 2024 IEEE.

关键词： Lung cancer

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