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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in Engineering & sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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Rotten and Fresh Fruits Classification using Deep Learning 5

Rotten and Fresh Fruits Classification using Deep Learning

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mishra, Prashant Kumar Singh, Jagrati Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350383522

Accurate and timely classification of fresh and decaying fruits is crucial for upholding quality standards in the food industry. This study introduces a deep learning approach that utilizes images to distinguish between fresh and rotten fruits. The proposed solution effectively addresses this classification challenge by control the capabilities of Convolutional Neural Networks. In this research a diverse dataset is used, comprising labeled images of various fruits in varying states of freshness, encompassing both rotten and fresh examples. Key preprocessing steps, including resizing and normalizing the dataset, is implemented to ensure that the model receives a uniform input. The core of the methodology involves initializing a pre-trained CNN architecture. This approach enables the adaptation of the pretrained model to the specific fruit classification task, ultimately providing an efficient and effective solution for quality control in the food sector. Through the utilization of this approach, our model can inherit valuable feature representations from its pretrained counterpart. To evaluate the performance of the trained model, a distinct test dataset is employed and the results encompass key measurements of performance. With an impressive accuracy of 97.5%, the proposed model outperforms the base paper, which achieves 96.05%. This advancement highlights the model's proficiency in distinguishing between fresh and rotten fruits, underscoring its practical effectiveness. © 2024 IEEE.

关键词： Convolutional Neural Networks KNN Rotten and Fresh Fruits Classification VGG16

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LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3517-3536页

作者： Lijo, Jithy Janardhanan Subramanian, Saleema Department of Computer Science Christ University Karnataka Bengaluru India Department of Statistics and Data Science Christ University Karnataka Bengaluru India Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Karnataka Bengaluru India

Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Biwolf optimization Feature extraction LP Norm Regularized deep CNN Pre-processing Yolo V5

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TrA-MHWR: Transformer-Enhanced Feature Extraction with Attention-Based Recognition for Offline Handwritten Malayalam Words 2

TrA-MHWR: Transformer-Enhanced Feature Extraction with Atten...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Anju, A.T. Chacko, Binu P Mohamed Basheer, K.P. Sullamussalam Science College Department of Computer Science Malappuram India Prajyoti Niketan College Department of Computer Science Trisssur India Amal College of Advanced Studies Department of Computer Science Malappuram India

ISBN: (纸本)9798331534950

The article introduces TrA-MHWR a unique methodology transformer-based model designed to facilitate the recognition of handwritten Malayalam words in an offline setting. The model features remarkable ability to transform raw pixel data into meaningful linguistic units. Utilizing the power of Vision transformers and attention units, the model offers a sophisticated framework for robust and precise identification of handwritten Malayalam words in offline contexts. This study presents a comprehensive analysis of the architecture, training methodology, and evaluation results of TrA-MHWR, demonstrating its efficacy in pushing the boundaries related to the recognition of handwritten words in relation to Malayalam language. The proposed study uses DeiT model with a multi-head self-attention layer to improve feature sensitivity and achieved 98.82% accuracy. © 2024 IEEE.

关键词： Optical character recognition

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Data-Driven Approaches for Early Detection and Prediction of Chronic Kidney Disease Using Machine Learning 5

Data-Driven Approaches for Early Detection and Prediction of...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mathai, Shibi Thirunavukkarasu, K.S. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798400709418

In recent years, the application of machine learning (ML) techniques for medical diagnostics has shown promising advancements. This study introduces a distinctive method for predicting chronic kidney disease (CKD) harnessing the prowess of ML. Our methodology encompasses an innovative data preprocessing approach, intricate feature engineering, and an amalgamation of ensemble techniques for model training. By evaluating our model on a dataset sourced from Kaggle, comprising 400 samples, we achieved an impressive accuracy of 98%, outperforming traditional methods. The findings underscore the potential of ML in revolutionizing CKD diagnostics, laying a foundation for further exploration in this domain. © 2023 ACM.

关键词： Diagnosis

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Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN 5

Named Entity Recognition for Protecting Sensitive Data using...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Gowr, P. Sheela Kumar, N. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall. © 2023 IEEE.

关键词： Natural language processing systems

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Deep Learning-Based Alzheimer's Disease Classification using Transfer Learning and Data Augmentation 3

Deep Learning-Based Alzheimer's Disease Classification using...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Anand, Deva Gowr, P. Sheela Logesh, S. Anand, S.M. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise for AD classification. This research study explores the effectiveness of transfer learning in training models based on deep learning for the classification of Alzheimer's condition. We trained several transfer models using DenseNet121, InceptionV3, Xception, and ResNet101. Based on performance evaluation, InceptionV3 was selected as the base model, outperforming in comparison to the accuracy of the other models. To enhance the accuracy of the InceptionV3 base model, we added capacity, tuned hyperparameters using Keras tuner, and utilized data augmentation techniques. The final model was trained on the Kaggle Alzheimer's Dataset, consisting of 4 classes of images, and achieving an AUC value of 87%. Our research demonstrates that transfer learning and other data augmentation approaches are useful in improving the accuracy of deep learning models for the categorization of Alzheimer's condition. This research has practical implications for improving public health outcomes, facilitating timely intervention and effective treatment. It contributes to the development of more accurate diagnostic tools for AD and can help address the challenges associated with this disease. © 2023 IEEE.

关键词： Neurodegenerative diseases

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A Comparative Study on the Application of Text Mining in Cybersecurity

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Recent Advances in computer science and Communications 2023年第3期16卷 80-93页

作者： Barik, Kousik Misra, Sanjay Konar, Karabi Kaushik, Manju Ahuja, Ravin Department of Computer Science JIS Institute of Advanced Studies & Research JIS University Kolkata India Department of Computer Science and Communication Ostfold University Halden Norway Department of Computer Engineering Amity University Jaipur India Center of ICT/ICE Research CUCRID Covenant University Ota Nigeria

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: The amount of data generated worldwide has been attributed to a change in different activities associated with cyber security, and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 2021. Out of which, 75 papers are selected for analysis. A detailed evaluation of the selected studies employs sources, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future studies, such as text processing, availability of datasets, innovative methods, and intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications;the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications. © 2023 Bentham science Publishers.

关键词： Data mining

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Reinforcement Learning Technique Based Automated Feature Analysis of Gene Expression Data

Reinforcement Learning Technique Based Automated Feature Ana...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Jayakrishnan, R. Meera, S. Vels Institute of Science Technology and Advanced Studies Computer Science & Engineering Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350333244

Immunohistochemistry and reverse transcription polymerase chain reaction, it is feasible to find molecular markers that are specific to cancer;nevertheless, there are no pathognomonic molecular markers available for the vast majority of solid tumors at this time. In this paper, we develop a Reinforcement learning Technique based automated feature analysis of gene expression data. This provides support for the idea that, on average, all the validation findings and training data from each epoch were utilized. This proposed method helps to acquiring knowledge at a more leisurely pace is, in the end, what makes episodic deep RL possible as a potential choice for students to pursue. To put it another way, the slow but steady accumulation of knowledge paves the path for the rapid extension of one existing body of information. The proposed methods provide 98 percent of the accuracy result than existing ANN and DNN. The proposed framework outperformed existing methods in terms of increase accuracy rate and reduced execution time than other methods © 2023 IEEE.

关键词： Reinforcement learning

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Classification of Soil Moisture Content with the Application of Deep Learning

Classification of Soil Moisture Content with the Application...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Ansari, Mohammad Shaharyar Gautam, Prateek Raj Centre for Advanced Studies AKTU Department of Computer Science and Engineering Lucknow India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798350364088

India is an agricultural-oriented nation where soil moisture plays a vital role in the growth of plants and crops. Measuring soil moisture and maintaining a balance in the soil moisture content help sustain water resources. The traditional method for calculating the soil moisture content is to calculate the weight of moisture soil and dry soil differences. With time passage, different techniques are introduced based on different sensors. Recently, machine learning and deep learning have emerged as powerful tools and can be used for the classification of different levels of soil moisture. Convolution Neural Network is a technique used in the field of deep learning for the classification of images. This algorithm is used to analyze the images on various factors like texture, color, hue, and shapes. In this research, images have been collected from the internet image search and stored in separate folders according to their class. CNN is applied to the created dataset and after tuning the hyperparameters model reaches an accuracy of 73%. © 2024 IEEE.

关键词： Soil moisture

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