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Efficient Model for Multiview classification for diagnosis of Brain Tumors.

Efficient Model for Multiview classification for diagnosis o...

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Anil Kumar, B. Chandrakala, B.M. Shruthi, B.V. Rv Institute of Tech & Management Dept of Computer Science & Engineering Bangalore India Dayananda Sagar College of Engineering Dept of Information Science & Engineering Bengaluru India Nitte Meenakashi Institute of Technology Dept of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306088

Brain tumors are caused mainly by DNA abnormalities, which result in aberrant cell division, prolonged cell lifespans, and the creation of aggregative masses inside the brain. Such tumors prevent the brain from operating at its best, sometimes resulting in long-term problems. These tumors' genesis (primary or secondary) and malignancy (benign or malignant) are used to classify them. While some might not be immediately lethal, their growth or contact with healthy brain tissue might cause damage. Investigations into various approaches, such as conventional medical imaging and artificial intelligence (AI), mainly using deep learning (DL) and transfer learning (TL), is prompted by efforts to identify and categorize brain tumors. Implementing the EfficientNetB0 model, which is famous for its adaptability and efficacy, is an excellent demonstration of this strategy. This work uses a well-curated dataset of 3264 photos from various classes to closely analyze the use of the EfficientNetB0 TL model to identify and classify brain tumors from MRI scans. The model's adaptability and accuracy are meticulously examined using a dual-pronged strategy incorporating transfer learning and fine-tuning, yielding an astounding accuracy of 98.5%. Accuracy and loss charts illustrate the model's learning development in vivid detail, and a well-defined confusion matrix provides extensive insights into categorization effectiveness across various tumor classifications. The achievement of a 98.5% accuracy rate highlights the potential paradigm change made possible by AI and TL in the diagnosis and classification of brain tumors. This accomplishment confirms the efficacy of such sophisticated procedures and promises to speed up diagnosis, enable prompt therapies, and improve patient prognoses. The combination of AI and TL heralds a new era of precision medicine, ready to reshape the field of brain tumor care as research in this area grows. © 2023 IEEE.

关键词： Brain

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Transformer-Based Single-Cell Language Model:A Survey

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Big Data Mining and Analytics 2024年第4期7卷 1169-1186页

作者： Wei Lan Guohang He Mingyang Liu Qingfeng Chen Junyue Cao Wei Peng Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronic and InformationGuangxi UniversityNanning 530004China School of Computer and Electronic Information Guangxi UniversityNanning 530004China College of Life Science and Technology Guangxi UniversityNanning 530004China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention *** addition,increasing studies based on transformers have been proposed to model single-cell *** this review,we attempt to systematically summarize the single-cell language models and applications based on ***,we provide a detailed introduction about the structures and principles of ***,we review the single-cell language models and large language models for single-cell data ***,we explore the datasets and applications of single-cell language models in downstream tasks,such as batch correction,cell clustering,cell type annotation,gene regulatory network inference,and perturbation ***,we discuss the challenges of single-cell language models and provide promising research *** hope this review will serve as an up-to-date reference for researchers who are interested in the direction of single-cell language models.

关键词： language model transformers deep learning single-cell data

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Classification of Nutritional Deficiencies in Cabbage Leave Using Random Forest 11

Classification of Nutritional Deficiencies in Cabbage Leave ...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Nupa Ram Shukla, Ratnesh Kumar Sengar, Alok Singh Gupta, Anurag College of Computing Science and Information Technology Computer Science and Engineering UP Moradabad India Shambhunath Institute of Engineering and Technology Computer Science and Engineering UP Prayagraj India

ISBN: (纸本)9781665487320

Now a day agriculture is very important in India since it is a growing nation. But generally the crop production attained by farmers would be much below the optimal production. It is very important to correctly detecting and identifying the crop diseases to enhance the profit of the formers and the stakeholder. The main reason for the crop production gap is due to the lack of essential soil nutrients and irrigation in the agricultural farms. To escalate the crop production, it is essential to balance the chemical elements or nutrients present in the soil with varying parameters of soil like the pH and soil moisture. Crop productivity can be increased to optimum level by efficient soil nutrient management. In case of Nutrient deficiencies, visual symptoms will appear on the leaf. This paper put forwards a method to identify the nutrient deficiencies of plants by making use of visual symptoms appearing on the leaves by Classification. Eight types of deficiencies i.e. N, P, K, Ca, B, Zn and Mg will be studied. The proposed study consists of creation and pre¬processing of a set of images consisting of nutrient deficient and healthy leaves, feature extraction and by using Random Forest performing multi class classification of nutrient deficient leaves. Evaluation of tomato leaf from the dataset focuses on recognizing the visual detection and indications of nutritional deficiencies. The proposed architecture achieves the 98.30% accuracy with the model size of 9.20 MB. © 2022 IEEE.

关键词： Nutrients

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Text-Based Sentiment Analysis: Experimentation with CNN and Basic Machine-Learning Approaches

Text-Based Sentiment Analysis: Experimentation with CNN and ...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Agrawal, Umang Kumar Das, Debashreet Ramana, B.V. Panda, Nibedan School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Centre for UG and PG Studies BPUT Odisha Rourkela India Department of Information Technology Aditya Institute of Technology and Management Andhra Pradesh Tekkali India

ISBN: (纸本)9789819613472

Sentiment analysis, a burgeoning sector within natural language processing (NLP) plays a crucial role in discerning and perceiving attitudes, emotions and opinions expressed in the form of textual data in the social media platform. With the growing proliferation of social networks, sentiment analysis has become indispensable for the government, firms, and researchers, providing beneficial insights into customer feedback, public perception and new actuality. Over the years, Convolutional Neural Networks (CNN) have emerged as the most influential approach in NLP to implement text classification. In this article, we have incorporated Deep Neural Network (DNN) namely CNN in contrast with various basic supervised machine-learning (ML) approaches evaluated on the IMDB Movie Review Dataset. We have analyzed the pros and cons of each approach, simplifying their efficiency in analyzing the sentiments to compel the manifest model into a prolific and reliable approach for better text classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Exploring Multimodal Framework of Optimized Feature-Based Machine Learning to Revolutionize the Diagnosis of Parkinson’s Disease: AI-Driven Insights

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Biomedical Materials and Devices 2025年 1-20页

作者： Khanom, Fahmida Mostafiz, Rafid Uddin, Khandaker Mohammad Mohi Department of Mathematics American International University-Bangladesh 408/1 Kuratoli Khilkhet Dhaka-1229 Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering Southeast University Dhaka China

Parkinson’s disease (PD), a progressive neurodegenerative disorder, poses significant challenges to healthcare systems worldwide due to its increasing prevalence and need for early detection to improve patient outcomes. This study introduces a robust diagnostic framework leveraging a multimodal feature selection approach, combining Minimum Redundancy Maximum Relevance (mRMR) with Recursive Feature Elimination (RFE), to optimize predictive accuracy in detecting PD. Using a dataset of 894 instances from the UCI Machine Learning Repository, the framework implemented comprehensive data preprocessing and feature selection techniques to refine input features. The Extra Trees classifier, integrated with the mRMR-RFE multimodal feature selection method, demonstrated superior performance over nine other machine learning models, achieving a perfect accuracy of 100%, with high specificity, sensitivity, precision, and F1 scores. This framework highlights the efficacy of ensemble learning methods and feature optimization in healthcare, ensuring computational efficiency and high predictive reliability. By addressing the challenges of dimensionality reduction and ensuring precise predictions, the proposed model sets a benchmark for machine learning applications in PD diagnosis. The results emphasize the potential of this framework to facilitate early detection and support clinical decision making, with significant implications for advancing AI-driven healthcare systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Extra trees classifier Machine learning Parkinson’s disease Recursive feature elimination (RFE)

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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A Transfer Learning Approach for Neurodegenerative Disease Classification from Brain MRI Images: Distinguishing Alzheimer's, Parkinson's, and Control Cases 6

A Transfer Learning Approach for Neurodegenerative Disease C...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Siddiqua, Ayesha Oni, Atib Mohammad Miah, Md. Jarez Northern University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Electrical & Electronic Engineering Dhaka Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798350385779

Like many other critical medical conditions, different neurogenerative diseases, including Alzheimer's and Parkinson's diseases, need to get diagnosed in the primary stage. Deep learning algorithms show excellent performance in detecting neurodegenerative diseases from images obtained by magnetic resonance imaging (MRI) of the human brain. Recently, transfer learning has also shown promising outcomes in the classification of neurological conditions using brain MRI data. Here, we examine the efficacy of the transfer learning strategy utilizing four distinct CNN architectures, namely EfficientNetB0, ResNet50, InceptionV3,and Xception. Used dataset for the study has three classes;Parkinson's disease (PD), Alzheimer's disease (AD), and control (healthy). The study compares the accuracy, precision, recall, and F1- score metrics for the investigated CNN models. The result demonstrates that the EfficientNetB0 model shows the best training and testing accuracy, reaching an accuracy as high as 99.4%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Ship Detection and Recognition Based on Improved YOLOv7

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computers, Materials & Continua 2023年第7期76卷 489-498页

作者： Wei Wu Xiulai Li Zhuhua Hu Xiaozhang Liu School of Information and Communication Engineering Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China

In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of *** improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the *** paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different ***,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and *** distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world *** experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.

关键词： Ship position prediction target detection YOLOv7 data augmentation techniques

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Ontology-Based Data Modeling for NoSQL Databases: A Case Study in e-Healthcare Application

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SN computer science 2023年第1期4卷 3页

作者： Sen, Poly Sil Mukherjee, Nandini Department of Information Technology Techno Main Salt Lake Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India

With the advent of e-healthcare applications, managing health data manually is no longer possible. Health data being voluminous, complex in nature and generally described with specialized terminologies, requires to be specified with standards like ISO EN13606RM, openEHR, HL7, etc. for reference implementation and data storage. These standards are not completely interoperable and cannot fit to sensor data, cloud and database implementation for various requirements. Thus, a generic ontology is necessary to accommodate basic clinical care. This paper first proposes one such ontology and describes the process of development of the ontology as suggested in the literature. Next, the paper also proposes an algorithm which uses a domain ontology and a set of queries to output the design of a NoSQL data store. The ontology developed in the paper is used by the proposed algorithm to output a NoSQL design for storing semi-structured and unstructured healthcare data. The performance of the algorithm is also analyzed theoretically using the NoSQL database schema generated for healthcare domain. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Big data e-Health data e-Health sensor Healthcare ontology NoSQL data model

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A Bilevel Hybrid Genetic Algorithm for Capacitated Electric Vehicle Routing Problem 13

A Bilevel Hybrid Genetic Algorithm for Capacitated Electric ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Feng, Chang-Tao Jia, Ya-Hui Yang, Qiang Chen, Wei-Neng Jiang, Huaiguang School of Future Technology South China University of Technology Guangzhou China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

As electric vehicles become more prevalent, a novel vehicle routing problem (VRP) has emerged, known as the capacitated electric VRP (CEVRP). CEVRP requires determining not only the service order of customers but also the charging plans for vehicles, thereby increasing the complexity of solution construction. In response to this challenge, we propose a bilevel hybrid genetic algorithm (BHGA). BHGA models CEVRP as two levels of subproblem: 1) the upper level capacitated VRP, focusing on the service order and 2) the lower level fixed route vehicle charging problem, focusing on the charging plans. In dealing with the upper level subproblem, the hybrid genetic search algorithm is adopted to construct the routes to visit customers and an advanced screening strategy is proposed to optimize the local search process and effectively guide the evolution of population. For the lower level subproblem, an efficient heuristic method called focus enumeration is designed, which is specifically used to insert charging stations into routes to ensure battery constraint. The collaboration of the advanced screening strategy and the focus enumeration assists in more unified solving of the two subproblems. The experiments show that BHGA significantly surpasses state-of-the-art algorithms on benchmark instances and has successfully updated eleven best known solutions, demonstrating its outstanding performance. © 2024 IEEE.

关键词： Heuristic methods

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