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2024 Frontiers in Optics, FiO 2024

作者： Ahmadsaidulu, Shaik Kanase, Akash Suresh Jain, Puneet Kumar Banoth, Earu Department of Biotechnology & Medical Engineering National Institute of Technology Odisha Rourkela769008 India Department of Computer Science & Engineering National Institute of Technology Odisha Rourkela769008 India

In this work a deep-learning model using enhanced YOLOv8(You Only Look Once) for classifying Acute Lymphoblastic Leukemia (ALL) and other normal *** 98% accuracy for ALL and 91% for combined (ALL & Normal) classif... 详细信息

关键词： Contrastive Learning

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Security System for Services Access in Smart City System Using Internet of Everything and Cloud 2

Security System for Services Access in Smart City System Usi...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Wadhwa, Amit Arora, Neerja G. L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350371055

With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture of Internet of everything. In a smart city the distinguish concept is the utilization of internet of everything architecture to effectively manage the services provided to users. Moreover, integration of internet of everything with cloud computing architecture would provide a better ecosystem in the area of smart cities. Providing services to smart city users could pose certain challenges and security issues with the integration of cloud computing. As per previous studied literature there is scope for improving access and authentication-based security of services imparted to smart city users. In this work, initially the consolidation of internet of everything (IoE) and cloud computing in smart cities is put forward. Further, this work focusses upon issues and challenges encountered in leveraging IoE in smart city environments with analysis of existing techniques for authentication and access control of cloud-based services and later provides a proposed system focusing upon imparting access control and authentication-oriented security for services delivered over a cloud and IoE followed by its analysis. © 2024 IEEE.

关键词： Access Control Authentication Cloud Computing Services IOE (Internet of Everything) Smart Cities

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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DESI: Diversification of E-Commerce Recommendations Using Semantic Intelligence 1

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6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Deepak, Gerard Anavkar, Harshada Vinay Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Due to the dynamism in the Web 3.0 there is need for semantically driven framework for ecommerce based recommendations. This paper proposes the DESI framework, which is a query-driven, semantically oriented, Web 3.0 conforming ecommerce recommendation framework. The pre-process query was enriched using the Latent Semantic Indexing. Ontologies are generated from the ecommerce product dataset. The classification of the metadata takes place using the LSTM classifier. The relevance computation is staged and is achieved using Lin similarity, Adaptive Pointwise Mutual Information measure. The semantics oriented reasoning on the basis of semantic similarity measures yield the matching products and Frog Leap algorithm. The normalized point wise mutual information measure is used to compute the intermediate results and probability algorithm ensures optimality computation from the initial feasible solution set. An overall precision accuracy measure of so and so on with the lowest value of False Discovery Rate has been achieved with a proposed framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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CKGLD: Curating Knowledge Graph for Linked Open Data Generation for Cyber Law and Ethics 4th

CKGLD: Curating Knowledge Graph for Linked Open Data Generat...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Venugopal, K.R. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India National Institute of Technology Tiruchirappalli India Bangalore University Bengaluru India

ISBN: (纸本)9789819757022

This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically acquired knowledge from Web 3.0. With advanced methods of term and category extraction, classification using both lightweight machine learning and strong deep learning methods, and clustering, the framework can exceed all similar frameworks in terms of performance. Initial terms are processed from the documents using the TF-IDF strategy. Static metadata stack is established from e-books, query logs, and glossaries. Renyi entropy is used to guide informative term selection for a decision tree classification;in this manner, the scale of the generated knowledge is managed by lightweight computing requirements. Dynamic knowledge from Web 3.0 is classified using GANs and is subjected to DBSCAN clustering. A multi-agent setup with parallelization enhances the efficiency of the clustering process. The knowledge graph is finally created using the Co-SIM rank and is evaluated for specificity using Hellinger's distance and volume of intersection index. These paradigms contribute to this framework significantly outperforming the baseline frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Block chain and Deep Learning Based Secure Communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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Judicial nested named entity recognition method with MRC framework

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 118-126页

作者： Zhang, Hu Guo, Jiayu Wang, Yujie Zhang, Zhen Zhao, Hansen School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Department of Computation Science and Engineering George Institute of Technology Atlanta United States

Judicial named entity recognition (JNER) is a basic task of judicial intelligence and judicial service informatization. At present, the research of JNER has attracted extensive attention. However, the existing JNER methods usually can only assign a single label to a token in the input sequence, which is not applicable to nested entities where a token may be assigned two or more different labels at the same time. Therefore, this paper introduces the machine reading comprehension (MRC) framework into JNER, and proposes a judicial nested NER method based on the MRC. Firstly, we design the question template according to the characteristics of judicial nested named entities, and construct the legal text named entity dataset in MRC format. Next, we introduce the span extraction MRC model based on the pre-trained to encode the question and text, and learn the context knowledge of the entity in the question. Finally, we extract the starting and end positions of the matching span respectively through two classifiers, to get the corresponding entities. The experimental results on the information extraction dataset in "CAIL2021" show, compared with the existing baseline models, the proposed method effectively improves the recognition effect of nested entities commonly existing in the judicial field. © 2023

关键词： Informatization

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Traffic Crash Severity: Predicting Property Damage and Injury Collision Through Machine Learning Models 7th

Traffic Crash Severity: Predicting Property Damage and Injur...

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7th International Conference of Transportation Research Group of India, CTRG 2023

作者： Agrawal, Toshini Mohan, Ranju Department of Computer Science and Engineering Vellore Institute of Technology Madhya Pradesh Bhopal466114 India Department of Civil and Infrastructure Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

ISBN: (纸本)9789819799428

The types of collisions during road crashes and the factors contributing to them are many. Chances of collisions can be reduced by predicting the probability of types of collisions at various locations and providing drivers with real-time details of the risk of collision while they travel. This paper aims to predict the severity of collisions at intersections, specifically two types of collisions—‘Property damage’ and ‘Injury Collision’, through machine learning models. The paper uses a dataset from the Kaggle website which the police department of Seattle regularly updates. It consists of values of different variables such as road condition, weather condition, junction type, and the attention span of the driver, hitting a parked car, light condition, and speeding. Four different conventional machine learning algorithms, namely, Random Forest, K-Nearest Neighbour, Decision tree, and Linear Regression are applied for collision prediction. The Random Forest model is observed to have the highest accuracy (0.7506) followed by the K-Nearest Neighbours algorithm (0.7442), Decision Tree (0.7385) and Linear regression (0.6999). The most featured attributes were weather conditions, road conditions and temperature and predicted more property damage than injury collision. An ensemble of the above models is also tried for crash prediction and observed as with the highest accuracy (0.7519). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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A Review on GIF-Based Steganography Techniques 5th

A Review on GIF-Based Steganography Techniques

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Chola, Abhinav Awasthi, Lalit Kumar Singh, Samayveer Department of Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789811998751

Data privacy has been a significant concern since the beginning of the Internet, and in the current scenario, the need and importance of data privacy are increasing exponentially. Today, a multitude of media formats including text, images, videos, Graphics Interchange Format (GIFs), etc., are available for exchanging data, and techniques of cryptography and steganography play a key role in maintaining data secrecy during such transmissions. This paper elaborates on the GIF-based techniques of steganography. It provides a detailed comparison of the working, advantages and limitations of the major approaches developed in this field in the last three decades. It provides a clear and chronological understanding regarding the evolution of GIF-based steganography methods and discusses future directions to enhance the existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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