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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Neelam Singh Manisha Upreti Ayushi Dwivedi Vandana Rawat Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

A species is biologically basic classification and taxonomic rank of an organism. Among the many common names for the mammals that make up the suborder Simiiformes (sometimes known as the simians), “monkey” is among the most common. The traditional classification of all members of the group currently known as simians as monkeys. Different classifiers are used to categorize the various kinds of monkeys based on their photos. Assigning a name or class to an image is the work of image classification. In the research paper, different image classifiers of Keras are used for image classification in monkey species. A dataset of 10 distinct monkey species was used to test out many different classifiers. Keras comes preloaded with a variety of Convolutional Neural Networks, including VGG16, VGG19, ResNet50, ResNet-50 V2, ResNeXt101, Inception V3, and Xception. We compared the performance of other classifiers and discovered that ResNeXt-101 achieved the best accuracy (99.98%).

关键词： Geometry Visualization Accuracy Software systems Organisms Biology Convolutional neural networks Information technology Image classification Residual neural networks

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Research on Deep Clustering Based on Image Data 13th

Research on Deep Clustering Based on Image Data

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13th International Conference on computer engineering and Networks, CENet 2023

作者： Li, Xuanyu Yang, Houqun Zhang, Xiaoying Yang, Dangui Huang, Jianqiang Gan, Lin College of Computer Science and Technology Hainan University Hainan Haikou570228 China Haikou Key Laboratory of Deep Learning and Big Data Application Technology Hainan University Hainan Haikou570228 China Hainan Haikou China Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette United States

ISBN: (纸本)9789819992386

Clustering is an important branch of unsupervised tasks, aiming at mining deeper relationships and patterns in data. The quality of feature representation based on image datasets often determines the upper limit of clustering tasks. In recent years, the application of deep learning in deep clustering representation learning module has learned better feature representation, which gradually overcomes the limitation of traditional shallow clustering facing high-dimensional unstructured data. Deep clustering has become a popular research direction in the unsupervised field in recent years, and high clustering performance has been obtained in the continuous deepening research. The existing deep clustering research is mainly oriented to various fields of artificial intelligence, including natural language processing (NLP), speech recognition (ASR), computer vision (CV), etc. We take the field of computer vision as the entry point, analyze and study the research progress of deep clustering in computer vision (CV), and deconstruct the deep clustering model into two parts: a detailed classification of the feature extraction module from the perspective of network model architecture, while the data space of clustering is used as the entry point to divide the clustering module, and discuss the deep clustering. Finally, we analyze the datasets and evaluation metrics commonly used in experiments to study deep clustering architectures in computer vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Feature extraction

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Structured Approach for Relation Extraction in Legal Documents

Structured Approach for Relation Extraction in Legal Documen...

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Global Conference for Advancement in Technology (GCAT)

作者： Anjali K Sasidharan R Rahulnath Department Of Computer Science And Application Amrita School Of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain’s rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain.

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Spam Email Detection using K-Nearest Neighbors: An Enhanced Approach

Spam Email Detection using K-Nearest Neighbors: An Enhanced ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sumit Saklani Kalyan Thapa Devendra Singh Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Spam emails represent a large portion of global email traffic and pose several risks, such as phishing, fraud, and malware attacks. Traditional spam filters are ineffective in combating spam that is ever-evolving, which makes the problems more complex in nature. Therefore, advanced machine learning techniques need to be adopted. This study attempts to integrate an optimized K-Nearest Neighbors (KNN) model for spam detection that implements Term Frequency-Inverse Document Frequency (TF-IDF) for text representation, along with Principal Component Analysis (PCA) for feature selection. Classification is performed with relevance to the neighbors with more distance using an adaptive distance weighting algorithm. Enhanced KNN techniques improve the metrics on Naive Bayes and Support Vector Machines (SVM) with respect to accuracy, precision, recall, and F1 score. Experimental results demonstrated the classification accuracy to amount 96.4 %, exceeds the existing systems. This study develops efficient spam detection systems having the capability of adapting novel spam methods.

关键词： Support vector machines Adaptation models Accuracy Computational modeling Unsolicited e-mail Heuristic algorithms Nearest neighbor methods Feature extraction Bayes methods Principal component analysis

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Sentiment Analysis on Airline Customer Review Using Language Model and Capsule Network 1st

Sentiment Analysis on Airline Customer Review Using Languag...

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1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Das, Nilanjana Dutta, Rakesh Mondal, Uttam Kumar Mandal, Jyotsna Kumar Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Application Hijli College West Bengal Kharagpur India Department of Computer Science and Engineering University of Kalyani Kalyani West Bengal Nadia India

ISBN: (纸本)9783031819346

In the past three decades, the competitive airline industry has experienced rapid growth. However, choosing a particular flight for travelling remains a crucial task and requires detail analysis and prior experience. These could be leveraged by intellectually processing existing passengers’ reviews or feedbacks. Traditional methods of collecting and analyzing such feedbacks or reviews are time consuming and repetitive. To address this challenge, sentiment analysis can be an effective tool. Twitter data has emerged as a valuable resource for collecting such reviews or feedbacks and conducting sentiment analysis. This research paper introduces a new deep learning model that combines BERT (a language model), self-attention based Bidirectional Gated Recurrent Unit with Capsule network, and Softmax classifier to assess airline passenger review tweets on six different US airlines, with three sentiment classes: positive, neutral, and negative. The performance of the sentiment analysis system is evaluated with various contemporary methods. This assessment demonstrates that the suggested model exhibits comparable performance to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Solution to Web Scraping

Solution to Web Scraping

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Chandan Biswas Rahul Mallick Subrata Paul Dipta Mukherjee Master of Computer Application University of Engineering and Management Jaipur Jaipur India Dept of Computer Science & Engineering University of Engineering and Management Jaipur Jaipur India

Data retrieval from a website, frequently automatically and without the owner’s consent, is known as scraping. This information can also be utilized however the scraper sees fit. The action is considered criminal, but the legality has changed because hasn’t stopped others from following suit. anti-scraping instruments are not used though. Anti-scraping Solutions are offered as fairly expensive services that are both slow, and effective services. This essay list offers suggestions for reducing impediments. the development of an anti-scraping System as a Product app for small- to medium-sized websites. are expensive.

关键词： computer hacking Instruments Microwave communication Microwave theory and techniques Internet of Things CAPTCHAs

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Research on Optimization Algorithm of Real Estate Enterprise Value Evaluation Based on BP Neural Network 2

Research on Optimization Algorithm of Real Estate Enterprise...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Niranjanamurthy, M. Saravanan, S. Mahaveerakannan, R. Surendran, S. Nakrani, Tulsidas Arun, M.R. Department of AI and ML BMS Institute of Technology and Management Avalahalli Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Tamilnadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India

ISBN: (纸本)9798350305418

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner. © 2023 IEEE.

关键词： BP neural network Enterprise worth appraisal Real estate

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A Review on Breast Cancer Classification using Neural Networks

A Review on Breast Cancer Classification using Neural Networ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Aashita Rajput Muskan Sachin Yadav Megha Chhabra Arun Prakash Agarwal Department of Computer Science & Application School of Engineering &Technology Sharda University Greater Noida India

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them.

关键词： Analytical models Computational modeling Biological system modeling Neural networks Breast biopsy Breast cancer Research initiatives

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