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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Jagannathan, J. Divya, C. Thanjaivadivel, M. School of Information Technology and Engineering Vellore Institute of Technology Vellore Dt Tamil Nadu Katpadi India Centre for Information Technology and Engineering Manonmaniam Sundaranar University Abishekapatti Tirunelveli India School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9781032669663

Human activities are causing global warming, which raises the heating of seas, the earth’s surface, and the atmosphere. Cities’ urbanisation leads to a rise in temperature and the formation of an urban heat island. Climate change forecasting has lately become a crucial demand for many industry and government agencies. Because of the dynamic swings in climatic circumstances, the researchers found it difficult to predict the climatic conditions with greater precision. However, when compared to other models, the accuracy of the forecast utilising machine learning approaches can be higher. Variation in land temperature has become a serious concern in today’s big metropolitan communities. Based on hourly temperature data acquired from the Indian Meteorological Department IMD over 117 years from 1900 to 2017, this work attempts to construct a SARIMA model to forecast monthly and daily temperature change. Temperature variation, autocorrelation function, partial autocorrelation, and residual distribution function were all used to assess the model’s correctness. Then, using the adfuller function and the error of determination for evaluation and root mean square deviation, determine if the series is stationary. In addition, the SARIMA model was used to anticipate average monthly and daily temperatures using historical data. The obtained data and model’s correctness, fitness, and timeliness are investigated, and the findings are linked. The monthly temperature trend in Chennai is researched and compared for the next years. The SARIMA (3,0,0), (0,1,1,12)),‘c’ model has an RMSE of 0.4811 degrees Celsius. The findings show that the SARIMA parameters were successfully matched, that the projected values match the true values, and that the seasonal pattern is also followed. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Global warming

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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

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Frontiers of Computer Science 2024年第4期18卷 39-47页

作者： Ziwang FU Feng LIU Qing XU Xiangling FU Jiayin QI School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Beijing 100876China Shanghai International School of Chief Technology Officer East China Normal UniversityShanghai 200062China School of Computer Science and Technology East China Normal UniversityShanghai 200062China School of Cyberspace Security Guangzhou UniversityGuangdong 510006China

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.

关键词： modality-fused representations cross-model blocks multimodal emotion recognition unaligned multimodal sequences computational affection

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Lightweight Blockchain Prototype for Food Supply Chain Management 17

Lightweight Blockchain Prototype for Food Supply Chain Manag...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Rusakov, Alexey Moradpoor, Naghmeh Akbarzadeh, Aida School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Gjovik Norway

ISBN: (纸本)9798331509736

The modern food supply chain often involves multiple layers of participants spread across different countries and continents. This complex system offers significant benefits to businesses worldwide;however, it also presents several challenges. One major problem is the inability to trace the product flow back to its origin, a critical issue in many industries. Another issue is the lack of trust among supply chain participants. Blockchain technology can help address these and other challenges faced by the supply chain industry. However, it is surprising that, globally, there are still not many examples of the technology's adoption, with most projects remaining in the pilot stage. This paper explores the field of custom blockchain design tailored to specific applications, with a focus on supply chain operations in the food industry. It includes the development of a lightweight yet fully featured Python prototype for a decentralized blockchain system. In addition to common features like block validation and state updates, the prototype includes a newly designed type of transaction tailored specifically for supply chain operations. These transactions eliminate the need for smart contracts, making the system more lightweight compared to general-purpose blockchain platforms such as Ethereum and less prone to security vulnerabilities. The prototype is designed as a public blockchain network, with Proof of Work selected as the consensus algorithm. The novelty of this research work lies in advancing the concept of a custom blockchain solution for the food industry. The key elements of the prototype have been unit tested. The overall evaluation was completed using a Python script that simulates product flow through an example supply chain, allowing product provenance to be determined by tracing the product flow back to its origin. © 2024 IEEE.

关键词： Food industry

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Visualization of Community Safety Threats with Time Series Evaluation 5th

Visualization of Community Safety Threats with Time Series E...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Gobi, N. Yadav, Renu Shankar, J. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electronic and Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The fast proliferation of connected gadgets on networks and the more and more pervasive use of technology in everyday lives is primary to more danger of cyber threats, making it vital to recognize the behavior of malicious actors as an excellent way to shield each structure and users better. Time collection analysis is a powerful tool to correctly understand and visualize the dynamic conduct of community security threats. This technique can help detect threats as they manifest within the community environment and provide insights into the underlying patterns and tendencies of malicious sports. This era allows us to appropriately come across, classify, and tune safety threats and anomalous community behavior in real-time. With the aid of visualizing the temporal styles of community protection threats, directors can benefit from a green and comprehensive view of the threat panorama and reply quickly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： anomalous activities directors threats visualizing

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Leveraging Deep Transfer Learning for Automatic Discovery of Polyp in Colonoscopy Imagery 5th

Leveraging Deep Transfer Learning for Automatic Discovery of...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahamed, Mohamadi Begum Syed Riaz Savita Beemkumar, N. Singh, Arjun School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Electronic and Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

Deep Switch latest is a rising technology that uses contemporary pre-skilled convolution neural networks or CNNs as a starting point for further evaluation. It leverages already-educated deep networks to fast, and it should be analyzed big information units and has been implemented for numerous imaginative and prescient problems such as object and scene reputation, item detection, and segmentation. This era has been implemented to detect today’s polyps in colonoscopy picas automatically. It is a crucial task because timely detection of today's polyps can cause early diagnosis and treatment of trendy colorectal cancers, one of the leading causes of modern cancers demise worldwide. This paper describes a trendy deep switch technique to detect contemporary polyps in colonoscopy images automatically. Finally, we evaluate our proposed approach at the available Endoscope dataset and exhibit how our trained version can accurately locate polyps in real-international colonoscopy photos. Our trained model achieves country-modern day-the artwork accuracy, making it an able candidate for deployment in scientific settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Harnessing LLMs for Medical Insights:NER Extraction from Summarized Medical Text 15

Harnessing LLMs for Medical Insights:NER Extraction from Sum...

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

作者： Sasikala, D. Sudarshan, R. Sivasathya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Pondicherry University School of Engineering & Technology Department of Computer Science Pondicherry India

ISBN: (纸本)9798350370249

The rapid growth of medical text presents a significant challenge for healthcare professionals to stay updated with current research and clinical guidelines. Effective text summarization is essential to facilitate access to knowledge, improve clinical decision-making, and ultimately enhance patient outcomes. This study aims to fine-tune and assess a medical text summarization system utilizing large language models (LLMs). The objective is to produce concise, coherent summaries of medical literature, prioritizing clinical relevance. Additionally, automated named entity recognition (NER) is employed to extract vital entities from the summaries generated by GPT3, T5, PEGASUS LLMs. These LLMs are trained on benchmark PubMed dataset which are evaluated under standard metrics such as ROUGE (1, 2, L) and BERTScore metrics. The model pipeline demonstrates summarization and NER performance over precision of 6 5% on medical text data. The resulting entity-annotated summaries provide condensed representations, enabling efficient information extraction and knowledge discovery. This automated approach accelerates biomedical research by large-scale medical text analysis. © 2024 IEEE.

关键词： GPT3 large language model medical text summarization Named Entity Recognition PEGASUS T5

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Facial Expression Recognition Based on TripletLoss and Attention Mechanism 28

Facial Expression Recognition Based on TripletLoss and Atten...

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28th International Conference on Automation and computing, ICAC 2023

作者： Gou, Rongqiang Gai, Qiuyan Xu, Zhijie Xu, Yuanping Zhang, Chaolong Jin, Jin He, Jia Shi, Yajing School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9798350335859

The paper proposes a facial expression recognition method called Triplet-Loss Attention Network (TAN), which aims to address the problem of large intra-class and inter-class distances in facial expression recognition. The method uses a self-attention mechanism to calculate the weight of triplet expression samples, which helps in determining the influence of key regions on expression recognition. The method uses images with attention weights above a certain threshold as training samples to form new hard triplets. The distance between high-weight samples in the three sets of samples is calculated using the Mahalanobis distance formula, and the difference in distance between high-weight groups of triplets is calculated using the Maharaja Loss function. The Triplet Loss is mainly used as the loss function in TAN, and the model is jointly optimized using Triplet Loss, Mahalanobis Loss, and Cross Entropy Loss functions to improve the performance of the model in facial expression recognition. Experimental results show that TAN performs well in alleviating the intra-class and inter-class distance problem and has good robustness and generalization performance. On the RAF-DB and FERPlus datasets, TAN achieves recognition accuracies of 88.40% and 88.73%, respectively, which is 1.37% and 0.18% higher than the previous state-of-the-art methods. © 2023 IEEE.

关键词： Face recognition

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Innovative Image Processing Techniques for Automating Cosmetic Inspection 5th

Innovative Image Processing Techniques for Automating Cosmet...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Panwar, Govind Singh Murthy, D.H.R. Agarwal, Trapty Jaison, Feon Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Progressive photograph processing techniques were developed for automating the technique of beauty inspection. These techniques make use of the wealthy, unique image records of the inspected objects to hastily become aware of diverse product defects. Specifically, object features including coloration, texture, size, shape, and location are first extracted from the photograph, and every function is then evaluated to come across cosmetic defects. Following the detection technique, the extracted capabilities are used to at once alter the manufacturing technique, enabling faster production turnaround times and progressed product quality. The strategies are designed to make certain rapid and accurate product inspection and also provide a bendy adjustment of parameters for various types of cosmetic merchandise. With these techniques, producers are capable of reduce cosmetically based totally product defects and supply improved great merchandise in a shorter time frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

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