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arXiv 2025年

作者： Liu, Yao Liu, Zhilan Tan, Tien-Ping Li, Yuxin Department of Management and Media The Engineering and Technology College Chengdu University of Technology Sichuan Leshan614007 China School of Computer Sciences Universiti Sains Malaysia Penang Penang11800 Malaysia Department of Art Design The Engineering and Technology College Chengdu University of Technology Sichuan Leshan614007 China

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection. © 2025, CC BY-NC-SA.

关键词： Graph neural networks

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Low Power and Enhanced Data Retention Time in DRAM in FinFET technology

Low Power and Enhanced Data Retention Time in DRAM in FinFET...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： K Sathyasree K Kavin Kumar S Sivaselvi A V Santhosh Babu R Deebika B Banumithra Department of Electrical and Electronics Engineering Nandha Engineering College Erode Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode Computer Science and Design Erode Sengunthar Engineering College palakarai Thuduppathi Perundurai Department of Information Technology Vivekanandha College of Engineering for Women Tiruchengode Namakkal Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tholurpatti Post Trichy Dt Tamilnadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Modern technologies require huge amounts of storage space and fast access to data for uninterrupted high-quality audio and video processing. Random Access Memory (RAM) operates at higher frequencies and allows the CPU to access the files faster. It stores frequently required data so that it can be retrieved rapidly. RAM cells are classified into SRAM and DRAM. For storage SRAM cell uses two cross-coupled inverters and DRAM cell uses a capacitor. SRAM cells are more reliable but require more than 6 transistors for its storage, which consumes more area and power than the DRAM cell, where it uses less than 4 transistors for its storage. But DRAM suffers from frequent refresh issues, due to leakage across the capacitor. The leakage current is reduced to minimize the frequent refresh issue and for that several leakage reduction techniques are incorporated to design the DRAM cell to enhance the Data Retention Time (DRT). Modified Self Controllable Voltage Level Circuit (MSVL) based DRAM cells are proposed in this project. MSVL 3T and 4T DRAM cells have less leakage power and better hold performance than the conventional, Sleepy Keeper and SVL based DRAM cells. Various parameters such as dynamic power, leakage power, retention time and delay are measured and compared with other DRAM cells. 18 nm FinFET technology is designed using the CADENCE Virtuoso Tool. The proposed DRAM cell has superior hold performance than previous designs, according to simulation findings. The frequent refresh rate has been reduced and finally the power consumed due to it has also been minimized.

关键词： Power demand Capacitors Random access memory FinFETs SRAM cells Time measurement Delays

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Revolutionizing Cloud Resource Allocation: Harnessing Layer-Optimized Long Short-Term Memory for Energy-Efficient Predictive Resource Management

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷

作者： Sireesha, Prathigadapa Priyan, Vishnu S. Govindarajan, M. Rajan, Sounder Rajakumareswaran, V. Asia Pacific University of Technology & Innovation Malaysia Biomedical Engineering Kings Engineering College Irungattukottai Sriperumbudur Chennai India Department of General Engineering Velalar College of Engineering and Technology Tamilnadu Erode India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Dept of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India

INTRODUCTION: This is the introductory text. Accurate data center resource projection will be challenging due to the dynamic and constantly changing workloads of multi-tenant co-hosted applications. Resource Management in the Cloud (RMC) becomes a significant research component. In the cloud’s easy service option, users can choose to pay a fixed sum or based on the amount of time. OBJECTIVES: The main goal of this study is systematic method for estimating future cloud resource requirements based on historical consumption. Resource distribution to users, who require a variety of resources, is one of cloud computing main objective in this study. METHODS: This article suggests a Layer optimized based Long Short-Term Memory (LOLSTM) to estimate the resource requirements for upcoming time slots. This model also detects SLA violations when the QoS value exceeds the dynamic threshold value, and it then proposes the proper countermeasures based on the risk involved with the violation. RESULTS: Results indicate that in terms of training and validation the accuracy is 97.6%, 95.9% respectively, RMSE and MAD shows error rate 0.127 and 0.107, The proposed method has a minimal training and validation loss at epoch 100 are 0.6092 and 0.5828, respectively. So, the suggested technique performed better than the current techniques. CONCLUSION: In this work, the resource requirements for future time slots are predicted using LOLSTM technique. It regularizes the weights of the network and avoids overfitting. In addition, the proposed work also takes necessary actions if the SLA violation is recognized by the model. Overall, the proposed work in this study shows better performance compared to the existing methods. Copyright © 2024 P. Sireesha et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work i

关键词： Cloud computing

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Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network & Supervised Approach

Sarcasm Detection followed by Sentiment Analysis for Bengali...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Moumita Pal Rajesh Prasad Deptartment of Technology (DOT) SPPU Pune India ACED Alliance University Bangalore India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it’s hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%).

关键词： Support vector machines Radio frequency Sentiment analysis Social networking (online) Computational modeling Neural networks Motion pictures

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Advancements in Packaging Materials: Trends, Sustainability, and Future Prospects

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Circular Economy and Sustainability 2025年 1-32页

作者： Cordeiro, Alisha Hussain, Mariya Ramachandran, T. Beemkumar, N. Kumar, Raman Karthikeyan, A. Bupesh Raja, V.K. Thatoi, Dhirendra Nath Mahapatro, Abinash Nanda, Jajneswar Prakash, Chander Patil, Arun Y. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 + 91 - *** India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur India School of Mechanical Engineering Rayat Bahra University Punjab Kharar 140103 India Faculty of Engineering Sohar University PO Box 44 Sohar PCI 311 Oman Department of Mechanical Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Punjab Mohali 140307 India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Punjab Gharuan Mohali India Bio-inspired Design and Optimization Centre (BIODOC) Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Mechanical Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India

Packaging plays a crucial role in product transportation, storage, and safety, with materials evolving to meet modern demands. Plastic packaging dominates due to its versatility, while paper and metal alternatives are gaining traction due to recyclability and durability. Bioinspired packaging and damage-tolerant designs are emerging trends, with China leading in research. The COVID-19 pandemic has accelerated the demand for medical packaging, where the United States leads in innovation. Advances in biodegradable materials, including biopolymers, plant-based Fibers, and smart packaging, are shaping the future of the industry. While challenges such as cost, durability, and barrier properties persist, innovations in nanotechnology and active packaging are enhancing performance. The shift toward sustainability and the 3R principles—Reduce, Reuse, Recycle continues to drive research and industry transformation. Compliance with regulatory requirements is increasingly supported by emerging technologies, including IoT, blockchain, and cyber-physical systems. Innovations in medical packaging include nanotechnology for barrier protection, modified atmosphere packaging (MAP), and intelligent packaging (IP), enhancing product quality and safety. Key design considerations focus on tamper-evident features, sterilization, and user-friendly accessibility, especially for children and the elderly. Material selection prioritizes microbial sterility, barrier properties, and durability to ensure product integrity. Sustainability remains a critical focus, with increasing adoption of biodegradable materials and efforts to minimize environmental impact. Future research aims to address gaps in sustainable packaging, regulatory compliance, and advanced material development to further improve packaging efficiency, safety, and eco-friendliness in the medical and pharmaceutical industries. © The Author(s) 2025.

关键词： Biodegradable polymers Bioinspired packaging Medical packaging Smart packaging Sustainable packaging

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design of half adder using integrated leakage power reduction techniques

Design of half adder using integrated leakage power reductio...

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作者： Bindu Katikala, Hima Pavan Kumar, Thatha Manideep Reddy, Bhimavarapu V.V.Pavan Kumar, Bandireddy Ramana Murthy, G. Dixit, Saurav Department of Electronics and Communication Engineering Vignan's Foundation for Science Technology and Research 522213 India Department of E.C.E Department of Electronics and Communication Engineering Alliance College of Engineering and Design Alliance University Bangalore562106 India Peter the Great St. Petersburg Polytechnic University St Petersburg195251 Russia Division of Research & Innovation Uttaranchal University Dehradun248007 India

The necessity for the development of compact, portable, and reliable electronic devices of enhanced speed and efficiency has prompted the scaling of CMOS devices to be indispensable. However, the benefit of scaling CMOS devices comes at the cost of increased leakage current in circuits. The variance in power consumption by these circuits incites detrimental impacts on the operational characteristics of the entire device. So, in this work, a novel leakage power reduction technique obtained by combining the Leakage Control Transistor (LECTOR) approach and drain gating approach is proposed. Both these subthreshold leakage minimization approaches are prominently used in Complementary Metal Oxide Semiconductor (CMOS) devices for curtailing the leakage power. The effectiveness of the proposed Integrated Drain Gating Lector (IDGL) technique in mitigating the leakage power is ascertained by designing a half adder circuit. Hence the overall leakage power is of 3.16nW & delay 69.12 µs in 180 nm technology, and in low scale technology of 90 nm the same leakage power decline to 2.19nW & delay is 65.45 µs. © 2022

关键词： Adders

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Deep Learning for Time Series Classification

Deep Learning for Time Series Classification

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Rajesh Sharma R Akey Sungheetha Animesh Kumar Sharma L. Chandra Sekhar Reddy Bandaru Satya Lakshmi Sunanda Ratna. Kandavalli Department of Computer Science and Engineering School of Engineering Alliance University Bangalore India Department of Mathematics The ICFAI University Raipur Department of Computer Science and Engineering (Data Science) CMR College of Engineering & Technology Kandlakoya(v) Medchal Hyderabad Department of Computer Science Engineering Aditya College of Engineering Surampalem Andhra Pradesh Department of Electronics & Communication Engineering Sri Padmavati Mahila Visva Vidyalayam Tirupati Andhra Pradesh India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Time Series Classification (TSC) has been measured as the most demanding crisis in data mining. With the augment of temporal statistics accessibility, thousands of numerical algorithms have been planned since 2016 because of their ordinary temporal ordering; time series data can be found in all kinds of tasks that require some kind of human thinking process. In the recent past, time series categorization became one of the most important problems to be tackled because of various information-keeping categorization problems. Time series can be found in several applications of the present world from the health sector, to personal activity, and detection of anomalies. As temporal data has been made accessible in recent years many new zones are becoming very interested in time series applications and based on that newer numerical chain algorithms have been suggested. Many machines today can be performed without actually touching them and to serve this purpose this machine store a series of pictures that can analyze the user signals. Recognizing the correct signal from the set of pictures can be a time series classification problem

关键词： Accuracy Recurrent neural networks Scalability Time series analysis Standards organizations Hidden Markov models Organizations

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Efficient Software Vulnerability Detection with Minimal Data Size in 5G-IoT

Efficient Software Vulnerability Detection with Minimal Data...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Sumit Kushwaha Asha V B Santhosh Kumar Navdeep Singh S. Prabagar B Yamini Supriya Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Computer Applications New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Hyderabad Lovely Professional University Phagwara India Department of Computer Science And Engineering Alliance College of Engineering And Design Alliance University Bangalore Karnataka India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

This research addresses the security challenges arising from the widespread implementation of Internet of Things (IoT) networks in the industry, a situation exacerbated by the integration of artificial intelligence (AI) and 5G mobile communication technologies. While these IoT networks enhance industrial efficiency and convenience, their vulnerability to security breaches is heightened due to nonstandard protocol stacks and open-source software that lacks thorough vetting. Traditional AI-based vulnerability detection methods, with their high memory and computational demands, are unsuitable for IoT applications. In response, this study introduces a novel approach utilizing the Sampling Threshold Optimization Strategy. This method maximizes the training data size while preserving learning accuracy, enabling swift identification of software vulnerabilities in IoT systems. Experiments conducted on a software vulnerability classification dataset reveal that the effectiveness of the models is not significantly impacted by variations in ideal data sizes. Notably, the research establishes the minimum amount of data required to train a model without compromising performance. For instance, employing the random forest model with the sampling threshold optimization technique resulted in a 97.9% improvement in latency and conserved 85.2% of memory, 1% of the data while still achieving learning accuracy that is on par with 100% of the data. This innovative method provides a practical means to enhance the security of IoT networks without the need for extensive processing power

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Effective Task Scheduling based on Candidate Optimization Algorithm (COA) in Heterogeneous NoC-Based MPSoC

Effective Task Scheduling based on Candidate Optimization Al...

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Smart Technologies, Communication and Robotics (STCR)

作者： Sathis Kumar K Janani T Karpagavadivu K Raihana A Meenalochini M Department of Information Technology Bannari Amman Institute of Technology Erode India Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Design Kongu Engineering College Erode India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Efficiency of the network communication and performance of the Network of Chips (NoC) with respect to the Quality of Service is greatly affected by the contention of the shared resources. Here, in this paper the design of MPSoC (MultiProcessor System on Chip) is done which is based on Network of Chips involves two steps – scheduling the sub tasks with respect to the Processing elements (PE) and further mapping the PE to the switching nodes which is present in the NoC topology. The evolutionary computation technique is applied once the development of the task model is completed to complete the first step which in turn helps in achieving less transfer cost as well as the running and execution time. The algorithm used in the proposed approach is the Candidate Optimization Algorithm (COA). Based on the NoC topology minimal expected network transmission delay along with less resource and power consumption is used to achieve the second step. The results obtained out of the comparison shows the resources and power consumption which is preferred by the COA algorithm in designing the system.

关键词： Costs Power demand Network topology Scheduling algorithms Switches Scheduling Time measurement Topology IP networks Optimization

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StAGN: Spatial-Temporal Adaptive Graph Network via Contrastive Learning for Sleep Stage Classification

StAGN: Spatial-Temporal Adaptive Graph Network via Contrasti...

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2023 SIAM International Conference on Data Mining, SDM 2023

作者： Chen, Junyang Dai, Yidan Chen, Xianhui Shen, Yingshan Luximon, Yan Wang, Hailiang He, Yuxin Ma, Wenjun Fan, Xiaomao College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science South China Normal University Guangzhou China Department of Electrical and Computer Engineering New York University New York United States School of Design The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781611977653

Sleep stage classification is a critical concern in sleep quality assessment and disease diagnosis. Graph network based studies for sleep stages classification have achieved promising performance. However, these studies still ignored the importance of learning morphological feature information with the spatial-temporal relationship among multi-modal physiological signals. To address this issue, we propose a Spatial-temporal Adaptive Graph Network named StAGN for sleep stage classification. The main advantage of StAGN is to adaptively learn the time-dependent and channel-wise interdependent waveform morphological features in multimodal physiological signals. Such features will be extracted by a modified 1-dimensional ResNet with a projection shortcut connection and adjusted by a joint spatial-temporal attention, thereby best serving the followed brain topological connection graph network for sleep stage classification. Meanwhile, we leverage the contrastive learning scheme with label information to further improve classification accuracy without changing the signal morphology. Experiment results on two publicly available sleep datasets of ISRUC-S1 and ISRUC-S3 show that the proposed StAGN can achieve a competitive performance for sleep stage classification, which is superior to the state-of-the-art counterparts. Copyright © 2023 by SIAM.

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