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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Joshi, Sudarshan Bachkar, Akshay Awaje, Omkar Bhoir, Rhutuj Savitribai Phule Pune University Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350348460

The paper explores various techniques for identifying multilingual text similarities and basic image similarities, covering a wide array of methods and opportunities for comparing and evaluating textual and diagrammatic content. By delving into these categories and studying the pros and cons of various methods, the survey equips researchers, practitioners, and enthusiasts in the field of NLP with a deeper understanding of text similarity *** paper delves into tokenization, vectorization, and similarity metrics, among other approaches. It also discusses modelling techniques, such as LSA and LDA, text alignment algorithms, cosine similarity, BERT, and optimization techniques. The survey categorizes these techniques into corpus-based, knowledge-based, semantic, term-based, and character-based similarities. The survey aims to provide a comprehensive overview of the methods available for checking multilingual text similarity identification and its significance in contemporary text analysis. Index Terms - Similarity Techniques, Machine learning, Natural Language Processing. © 2024 IEEE.

关键词： Image Similarity Multilingualism Natural Language Processing Semantic Analysis Similarity Techniques

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Leveraging ML Techniques for Image-based Freshness Index Prediction of Fruits and Vegetables 5

Leveraging ML Techniques for Image-based Freshness Index Pre...

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5th International Conference on Emerging Smart Computing and informatics, ESCI 2023

作者： Gokhale, Atharva Chavan, Ameya Sonawane, Sheetal Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665475242

Freshness is a prime factor of consideration when purchasing consumables like fruits and vegetables. Studies have proven that computer Vision can be successfully involved in classifying fresh and stale fruits and vegetables and measuring their freshness to some extent. This work attempts to determine and analyze the freshness of fruits and vegetables from their images by proposing a Machine Learning methodology. The entire study was divided into two steps. The first step focused on obtaining classification between images of fresh and stale fruits and vegetables. For this, we trained the ConvNeXt model on an open-source imagery dataset consisting of 12 classes, and it proved efficient by achieving an accuracy of 99.77%. The second step focused on analyzing how fresh a particular fruit or vegetable is from its image. We achieved this by using an open-source dataset of tomato images and extracting features specific to the texture, shape, and colour from these images. Further, we trained classification models on these extracted features and presented the results as quantitative measures with scores of 10 for each of these three factors. Thus, we attempted to achieve an in-depth freshness analysis by grading the images based on these three critical factors while defining freshness. © 2023 IEEE.

关键词： computer vision

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Localize the Copy-Move Forged Region of an Image Using Improved SIFT

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SN computer Science 2024年第1期5卷 71页

作者： Dhanaraj, Rachel Selva Sridevi, M. Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

With the advancement in technology, digital images have become a very popular source of information. But this source of information is also being used to mislead people and society by altering the original image. One of the ways through which digital images are tampered with is the copy-move forgery method, wherein a part of an image is copied and pasted into another area inside the same image. To avoid and prevent any kind of anomalies, this paper focuses on ways to detect this copy and move forgery in an image using the keypoint-based approach in an efficient manner. The methodology involves detecting key points using Improved Scale Invariant Feature Transform (SIFT) based on which features are identified. Within the isolated descriptors, the deployed K-Nearest Neighbor (K-NN) classifier determines the closest match. Now in order to remove the outliers present in the features, Hierarchical Clustering is used to cluster similar descriptors. Experimental result has proved this methodology to be effective in detecting the forgery in an image for different kinds of related transformations. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Copy-move forgery Hierarchical clustering K-NN classifier Scale invariant feature transform

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography

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Proceedings of the National Academy of Sciences India Section B - Biological Sciences 2024年第2期94卷 423-438页

作者： Malathi, S.Y. Bharamagoudar, Geeta R. Department of Computer Science and Engineering KLE Institute of Technology Hubballi Karnataka Hubballi India

This is particularly true for the senior population, whose quality of life has been drastically reduced as a result of the increasing incidence of several health problems. Over 27 million people in the United States suffer from osteoarthritis of the knee (OAK), a painful condition that may severely limit mobility. When the articular cartilage between the tibia and femur in the knee is damaged, osteoarthritis of the knee develops. OAK symptoms include a loss of mobility and the inability to walk normally due to knee pain, detected using an X-ray. We detail here a novel method of evaluating the severity of knee osteoarthritis (OA) by X-ray analysis. Modern methods are comprised of pre-processing, feature extraction using a convolutional neural network (CNN), and classification with latent semantic modeling (LSM) (LSTM). Data from the osteoarthritis initiatives (OAI) database, which is available to the public, was utilized to evaluate the methodology proposed. The current method has been shown to be effective, and the OAI database has information on KL grade assessment for both knees. OAK is the subject of state-of-the-art, global observational investigation by experts using a program called OAI. This collection was created to serve as a one-stop shop for researchers seeking the scholarly materials they need to systematically examine OA indicators as a possible endpoint for the advanced stages of the illness. The statistics reveal a mean accuracy of 100%. When compared to earlier deep learning approaches, these outcomes are much superior. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2024.

关键词： Deep learning Knee osteoarthritis Radiography X-ray

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Multi-layered access control based auto tuning relational key implications in enterprise-level multi-tenancy

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Multimedia Tools and Applications 2025年第17期84卷 17805-17836页

作者： Henge, Santosh Kumar Rajakumar, R. Prasanna, P. Parivazhagan, A. Hu, Yu-Chen Chen, Wu-Lin Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation KL University AP Vaddeswaram India Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Chennai India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science Xitun District Tunghai University No. 1727 Sec. 4 Boulevard Taichung City407224 Taiwan Department of Computer Science and Information Management Providence University Taichung City Taiwan

This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure data confidentiality and integrity. Maintaining secure access control is a complex and critical task in multi-tenant cloud environments, and researchers have proposed innovative methods for implementing security, privacy, and authenticity. However, several challenges remain. The proposed research addresses these challenges by presenting a layered approach to multi-tenancy in an enterprise-level cloud environment. The study has three main objectives: (1) to describe and analyze the complexities of enterprise-based multi-tenancy and proposed technical solutions in a tabular format;(2) to propose multi-tenant test cases across application, software, infrastructure, and network as a service environment;and (3) to present a collaborative, relational optimal key-based multi-tenancy access control model. The proposed access control model involves integrating various secure layers, such as intranet, extranet, intra-extranet, and using feasible relational optimal keys to provide secure multi-tenant access control among various tenants of single and multiple enterprises. This model provides secure data transmission by integrating various multi-layer data filters and secure blades. The layered multi-tenancy approach offers many benefits and technical feasibilities to avoid security and privacy issues in enterprise-based multi-tenant environments. The process achieved a 93% secure mapping rate to authenticate all IN and OUT data access transactions among the various multi-tenancy of similar and distinct enterprises. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtualization

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DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPEECH WAVEFORM GENERATION 12

DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Benita, Roi Elad, Michael Keshet, Joseph Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

Diffusion models have recently been shown to be relevant for high-quality speech generation. Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the spectrogram to a waveform (i.e., a vocoder). This work proposes a diffusion probabilistic end-to-end model for directly generating the raw speech waveform. The proposed model is autoregressive, generating overlapping frames sequentially, where each frame is conditioned on a portion of the previously generated one. Hence, our model can effectively synthesize an unlimited speech duration while preserving high-fidelity synthesis and temporal coherence. We implemented the proposed model for unconditional and conditional speech generation, where the latter can be driven by an input sequence of phonemes, amplitudes, and pitch values. Working directly on the waveform has some empirical advantages. Specifically, it allows the creation of local acoustic behaviors, like vocal fry, which makes the overall waveform sounds more natural. Furthermore, the proposed diffusion model is stochastic and not deterministic;therefore, each inference generates a slightly different waveform variation, enabling abundance of valid realizations. Experiments show that the proposed model generates speech with superior quality compared with other state-of-the-art neural speech generation systems. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

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工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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Emotion Recognition from Electroencephalogram (EEG) Signals Using a Multiple Column Convolutional Neural Network Model

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SN computer Science 2024年第2期5卷 213页

作者： Jha, Sonu Kumar Suvvari, Somaraju Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

Emotions are vital in human cognition and are essential for human survival. Emotion is often associated with smart decisions, interpersonal behavior, and, to some extent, intellectual cognition. From the recent literature on emotion recognition, we understand that the researchers are showing interest in creating meaningful "emotional" associations between humans and machines;there is a demand for accurate and scalable systems to detect human emotional states, as emotion recognition is needed to understand the mental status of such persons who cannot communicate their emotions, such as disabled people, mentally challenged persons, etc. Therefore, EEG signals provide a non-invasive method to identify the emotions of these disabled humans. The research community has recently been very interested in employing electroencephalography (EEG) for emotion classification since end-users have wearable EEG systems that may offer a portable, cheap, and straightforward technique for identifying emotions. Deep learning models have recently been extensively used to extract characteristics and recognize emotions from EEG recordings. Apart from that, various papers were reviewed in this research. This paper presented a multiple-column CNN network with a leaky ReLU activation function on the EEG brain wave and DEAP datasets. Multiple scalp electrode locations are used to collect EEG signals, and each electrode offers spatially unique data. The network can detect spatial correlations and extract characteristics that depict the spatial distribution of brain activity by employing a multiple-column CNN, which simultaneously processes signals from many channels. This makes it possible for the model to recognize emotions using the spatial information in EEG data. The result analysis was evaluated on different CNN models, and it was observed that an accuracy of 98.10% was achieved on the EEG brainwave dataset and 81% on the DEAP dataset. © 2024, The Author(s), under exclusive licence to Sprin

关键词： Attention Convolution neural network (CNN) Deep learning EEG Emotion recognition GCNN Multicolumn CNN

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Thin film ferroelectric photonic-electronic memory

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Light(Science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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