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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Sridhar, J. Kumar, B. Pavan Soumya, G. Saravanan, A.V. Surendra, A. Shruthi, P. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor517583 India

ISBN: (纸本)9798350376685

Gastric cancer (GC) is a common and deadly tumor with poor prognosis. Early detection uses endoscopy, and further treatment requires pathological confirmation and CT scanning. AI systems can help due to a lack of pathologists globally. Most GCs have genetic instability, an early gastric cancer sign. A new classification system based on histology, genotype, and molecular phenotype aids early diagnosis, prevention, and treatment. The project uses deep learning, especially Gastronet, to assist in GC diagnosis, offering high accuracy without extra tests. © 2024 IEEE.

关键词： Endoscopy

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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Detecting Plant Disease and Suggesting Sustainable Solutions by using Deep Learning Technique 5

Detecting Plant Disease and Suggesting Sustainable Solutions...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Kumar, M.P. Rani, Tms Mekala Teja, C. Priya, A. Vishnu Vivek, K. Nithya, Mudduluru Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh Chittoor517583 India

ISBN: (纸本)9798350376685

Crop loss due to pests and diseases poses a significant challenge in global agriculture, resulting in substantial economic ramifications, with an annual loss of crops valued at Rs 50,000 crore. This project addresses the pressing issues surrounding disease identification and management in agriculture. To revolutionize disease detection, we are pioneering an innovative approach leveraging Convolution Neural Networks (CNNs). By harnessing the power of AI platforms in collaboration with government resources, our aim is to deliver effective and efficient solutions to combat plant diseases. Our overarching goal is to simplify the process of disease identification and treatment through the utilization of advanced technology. Our project harnesses various convolution neural networks, including VGG-16, ResNet, DeepNet, and YOLO V3. We intend to train our model using these convolutional networks and select the one that exhibits the highest accuracy for our specific project requirements. Beyond merely identifying the type of disease, our project also provides tailored remedies for treating these specific diseases. We leverage Chat GPT technology and collaborate with government platforms to achieve this objective. Additionally, we translate these remedies into local languages to ensure accessibility for those in need. Moreover, we employ text-to-speech conversion, enabling the vocalization of remedies. This empowers farmers to receive guidance audibly and take prompt action to mitigate crop diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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AI/ML in Preventive Healthcare: Building Multi-Disease Prediction Systems for Early Interventions 3

AI/ML in Preventive Healthcare: Building Multi-Disease Predi...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Juneja, Sonam Kaur, Gursimran Bakshi, Purnima Bhati, Bhoopesh Singh Chandigarh University Department of Computer Science and Engineering Gharuanm Mohali India Chandigarh University Department of Mathematics Mohali India Indian Institute of Information Technology Department of Computer Science and Engineering Haryana Sonepat India

ISBN: (纸本)9798331519582

In today's healthcare, it has become crucial to predict multiple diseases such as PCOD, breast cancer and cervical cancer that have become more important to improve patient care. The proposed system is to be used by healthcare professionals to predict multiple disease with given input datasets. The stated system learns using machine learning capabilities including XGboost and CatBoost, in an effort to predict the presence of these cancers by identifying parameters that suggest the existence of the cancer. The proposed study shows that XGboost performs better than CATBoost in terms of Accuracy with 95 percent, making it suitable for integration with existing health care systems and enabling real time predictions to give doctors more powerful tools for diagnosing and developing personalized treatment plans for cancer patients. © 2025 IEEE.

关键词： Diseases

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Q&AI: An AI Powered Mock Interview Bot for Enhancing the Performance of Aspiring Professionals

Q&AI: An AI Powered Mock Interview Bot for Enhancing the Per...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Joel Manuel, C.J. Sabi, Maria Benson, Merene Baburaj, Gokul Saritha, S. Rajagiri School of Engineering and Technology Department of Computer Science and Engineering Rajagiri Valley Kerala Kochi India

ISBN: (纸本)9798350354539

The Q&AI Mock Interview Bot is an application designed to enhance interview preparation for job seekers and aspiring professionals. This work aims to create an intelligent virtual interview platform that simulates real interview scenarios, offering users the opportunity to practice answering questions while receiving valuable feedback. The rationale of the Q&AI bot is its' innovativeness in generating questions. The questions generated for each user are exclusively from the user's resume uploaded to the bot. Henceforth, the questions will be very much relevant to a user. Users can respond to interview questions in real-time. The system evaluates the users' responses and gauges their confidence level, performs sentiment analysis and emotion detection. It also analyses the language proficiency and factual accuracy of users' answers. After each interview session, the bot generates a detailed scorecard. The Q&AI Mock Interview Bot empowers users to refine their interview skills, boost confidence, and receive constructive feedback. This paper presents the design and implementation of the tool for career development and interview preparation in an increasingly competitive job market. © 2024 IEEE.

关键词： Machine learning

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Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Shailendra Kumar Rahamatkar, Surendra Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Research into disaster relief is a field with a lot of potential, and predictions made about its future are based on historical data. It is a difficult task to analyze the long-term consequences of a disaster. Governments, NGOs, and other organizations struggle to make sense of disaster data. It is hard to assess the impact of different factors, such as education, employment, agriculture, and the social sector, on people living in affected areas. This research paper represents a framework to evaluate the long-term effects of disasters, by means of machine learning algorithms. The algorithms mentioned in this paper are Random Forest, Decision Tree, Gradient-boosted Decision Tree (GBDT), and XGBoost. It seeks to analyze historical disaster data, identify impacts, and predict the effects of similar disasters in the future. The framework also suggests preemptive measures and the impact on those involved. Finally, the proposed framework will be able to make long-term analyses and recommendations, using data from past events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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Research on Automatic Grading of Handwritten Homework in Intelligent Education Based on Model Fusion Approach 24

Research on Automatic Grading of Handwritten Homework in Int...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Zhang, Xiaoming Zhuang, Rundong Zhuo, Zheng Department of Computer and Science College of Information Engineering Beijing Institute of Petrochemical Technology Beijing China

ISBN: (纸本)9798400712692

With the popularization of intelligent education, more and more handwritten homework is required to be photographed and uploaded on the online platform for submission. Teachers need to grade the handwritten homework submitted by students on the platform. A large number of repetitive grading homework brings a lot of burden to teachers and online grading also causes damage to teachers' eyesight. However, the current research in this area only focuses on the recognition of single font symbols, and cannot effectively and automatically grade the answers submitted by students in the actual teaching environment that contains multiple types of text,symbol and image. To address this problem, we propose an automatic handwritten homework grading system based on a model fusion method to assist teachers in the automatic marking and grading of homework. The model binarizes the collected handwritten tasks to reduce factors such as ambient light. Then the KNN model is used to divide the binarized images into two categories: digital symbols and graphic assignments. The YOLO-CoordAtt model is proposed for graphic homework recognition and automatic grading. The model predicts the target position and category of the scoring points in the answers submitted by students, and scores them according to different step points. Finally, all the step points obtained by the students are added up to output the student's score. In daily teaching environments, there are usually a large number of mathematical symbols in homework. Due to the complex symbol structure and different writing styles, YOLO-CoorAtt is usually difficult to accurately identify them. We proposed CSP-Posformer to solve this problem. CSP-Posformer converts the mathematical symbol homework submitted by students into latex expressions and compares them with the latex expressions of the scoring points entered by the teacher. If they are the same, they will be given scores, realizing automatic grading of handwritten homework containi

关键词： Teaching

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Domestic Service Providing System

Domestic Service Providing System

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Joel Joyson, T. Primya, T. Karthikkumar, P. Muthukumar, S. Department of Computer Science and Engineering K.P.R Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350376913

This paper introduces a novel web application using technologies like Reactjs, nodejs and MongoDBdesigned to optimize home service systems. In response to the growing demand for efficient and equitable service provision, our platform integrates advanced algorithms to streamline the process. The Round Robin algorithm ensures equal opportunities for service providers by distributing tasks in a fair and balanced manner. By allocating jobs based on availability rather than favouritism, this algorithm promotes fairness in service allocation. By this, the service providers are automatically allotted. Additionally, our system incorporates a Rating Algorithm, which evaluates the service provider’s rating based on customer ratings. Moreover, the Charge Based on Experience algorithm adjusts service charges according to the experience level of the provider, incentivizing skill development and rewarding expertise. Through the synergy of these algorithms, our web application aims to revolutionize the home service industry by fostering fairness, transparency, and efficiency. By providing equal opportunities to service providers, empowering customers with reliable information, and promoting professional growth, our platform represents a significant step towards enhancing the quality and accessibility of home services in the digital era. © 2024 IEEE.

关键词： Industries Ecosystems Information age Equal opportunities Reliability Resource management Task analysis

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Logistic Regression based Sentiment Analysis System: Rectify

Logistic Regression based Sentiment Analysis System: Rectify

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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