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2024 international Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Choudhari, Pragati Rawat, Ruchira Al-Fatlawy, Ramy Riad Shrivastava, Anurag Yadavk, Kanchan Pratap, Arun School Of Computer Science Engg. & Applications D Y Patil International University Pune Akurdi India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Gla University Department Of Electrical Engineering Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

The abstract presents a see into our inquiry about on-target distinguishing proof in sedate disclosure utilizing Random Forest, displaying the transformative effect of machine learning. Leveraging different natural datasets, we utilized Random Forest nearby other calculations to foresee potential medicate targets. Our comprehensive assessment measurements, counting accuracy, precision, recall, and F1 score, emphasize the prevalent execution of Random Forest in comparison to Decision Trees, Support Vector Machines, and k-Nearest Neighbors. The results demonstrate a precision of 85%, accuracy of 86%, review of 84%, and an F1 score of 85% for Irregular Forest, certifying its viability in precisely recognizing promising sedate targets. The study contributes to the advancing scene of machine learning applications in biomedical research, emphasizing the potential of gathering learning procedures, especially Irregular Woodland, to streamline and improve the target identification stage of sedate improvement. This research adjusts with broader patterns in healthcare and pharmaceutical examinations, highlighting the flexibility of machine learning in tending to complex challenges and quickening the disclosure of novel restorative arrangements. © 2024 IEEE.

关键词： Decision trees

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Forecasting IPL Score Using Machine Learning Techniques

Forecasting IPL Score Using Machine Learning Techniques

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), international Conference on

作者： Riddhi Panchal Gopal Sakarkar Nilesh Shelke Amit Pimpalkar Department of Computer Science and Applications MITWPU Pune Maharashtra India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Department of Computer Science and Engineering (AIML) Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The IPL, which stands for Indian Premier League, has become one of the most widespread and fought over Twenty20 cricketers in the world, which borrows the interest of its audience with its ever-changing and chaotic nature. In this research paper, the researchers aim to employ various machine learning techniques to predict the cricket scores of an IPL match, recording valuable insights aimed at enhancing the understanding of the sport and providing useful insights for the sporting teams and the fans as well as the analysts. The work aims to develop and test computational intelligence models appropriate for estimating IPL match total scores using modern technologies such as machine learning and artificial intelligence based on historical match scores, player statistics and match context. Among the methods used were ensemble techniques, such as neural and regression networks, to investigate complex patterns within the dataset and increase accuracy in predictive performance. As part of modelling, we harnessed a dataset that covers several seasons of the IPL that includes performance indicators of players, match characteristics, and necessary background information like the weather and venue of the game. Models are evaluated using the same dataset, but the target for prediction was never seen during training, thus ensuring that the model was accurate, recall, precision, and overall performance were optimal during training. Standard metrics such as MAE-Mean Absolute Error, MSE-Mean Square Error and RMSE Root Mean Squared Error measure the predictability of models. Lastly, we compared the algorithms to identify those that better predict IPL cricket scores. Knowledge derived from our research is rich since it hails from cricket match result factors highlighting the need to consider a player's form, teamwork, and extra influences from the weather. From its ability to demonstrate the viability and effectiveness of machine learning to predict scores in the game of cricket's IP

关键词： Training Adaptation models Accuracy Computational modeling Machine learning Games Predictive models Prediction algorithms Sports Meteorology

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Low Light Image Enhancement: A Special Click

Low Light Image Enhancement: A Special Click

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Computational Intelligence, Communication technology and Networking (CICTN), international Conference on

作者： Shalini Kapoor Yojna Arora Nidhi Bansal Khushwant Virdi Firdous Sadaf M. Ismail Suraj Malik School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Sharda University Greater Noida Uttar Pradesh India Department of Computer Science & Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies (Deemed to be University) Faridabad Haryana India Department of Computer Science and Applications School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering IIMT University Meerut Uttar Pradesh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Photography and images are an important and integral part of today’s world, resulting in the development of multimedia and many other subjects comparable to image processing. The specifications of the camera and camera lens are just not enough hence image processing is always required. Low light photography, the problem gets even bigger because the camera lens fails to capture enough light and hence low light photography depends mostly upon image processing algorithms which help to reduce noise in the image, brighten the image for better overall visual quality and enlighten the hidden dark areas. Many efforts have been put into advancing image quality in little light, but none have given satisfactory results. This research report aims to analyses the techniques used for low- light image improvement and what advancements can be made to advance the image condition further.

关键词： Photography Image quality Visualization Histograms Image resolution Surveillance Noise Cameras Image enhancement Lenses

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Tensor Levenberg-Marquardt Algorithm for Multi-Relational Traffic Prediction

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IEEE Transactions on Vehicular technology 2023年第9期72卷 11275-11290页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kuan, Yen-Cheng Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan International College of Semiconductor Technology Hsinchu30010 Taiwan Communications Research Centre Canada OttawaONK2H 8S2 Canada

Traffic prediction serves as a crucial role in intelligent transportation systems for applications including routing planning and traffic control. With the advances of sensor technologies and Internet of Things (IoTs), the exploitation of traffic data motivates us to reformulate the traffic-state prediction problem based on the big-data characteristics. In order to infer multifarious traffic information, the standard one/two-relational traffic data in the vector/matrix form needs to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent multi-relational nonlinear dynamics which are often encountered in the realistic time-series analysis. Furthermore, there hardly exist convergence studies on convergence condition and convergence speed pertinent to the existing traffic-prediction methods. In order to combat the aforementioned challenges, we propose a new tensor Levenberg-Marquadt algorithm (TLMA) to conduct the multi-relational nonlinear regression for traffic prediction involving tensorized data. Meanwhile, we also derive the lower-bound of the number of iterations required to make the error-norm below a specified tolerance for global convergence. Besides, we derive the conditions for our proposed TLMA to converge to the optimal solution. The convergence rates with respect to different nonlinear cost functions have been derived theoretically. The computational- and memory-complexities of our proposed new TLMA are studied as well. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of our proposed new TLMA over real traffic data in comparison with other existing multi-relational prediction methods. © 1967-2012 IEEE.

关键词： Tensors

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Data Driven Mental Health Predicting Depression Using Machine Learning

Data Driven Mental Health Predicting Depression Using Machin...

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Data science and Business Systems (ICDSBS), international Conference on

作者： Nitesh Kumar Anamika Kumari Neha Kumari Krishna Murari Prakash Anand Richa Verma Department of Information Technology L.N. Mishra College of Business Management Muzaffarpur Bihar India Department of Computer Science and Engineering Amity School of Engineering and Technology Patna Bihar India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Computer Science in Artificial Intelligence Galgotias College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Applications International School of Management Patna Bihar India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The rise of information investigation methods such as machine learning is opening up unused openings in the field of mental wellbeing. This department of inquire about, known as data-driven mental wellbeing, is especially interested in applying machine learning to identify discouragement. Analysts trust to make models that can recognize individuals at chance of discouragement by analyzing information such as overview answers, social media action, and discourse designs. Early distinguishing proof is basic for his, her, their, etc. ideal treatment, and machine learning may play an imperative part in fulfilling this point. Be that as it may, it is basic to realize that these models are not aiming to supplant mental wellbeing pros. Instep, they are planning as disobedient to help in screening and intercession efforts.

关键词： Schedules Analytical models Social networking (online) Machine learning Mental health Data science Solids Depression Business

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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Decision Aid sciences and Application (DASA), international Conference on

作者： Mohammed Noori Mohsin Ahmed Amer Ibrahim Ali Saeed Mohammad Khishe Mahmoud Mahfuri Al-ma'moon University College Baghdad Iraq Department of Computer Science and Software Engineering College of Information Technology Computer Center Northern Technical University Ninevah Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer and information Technology Department International University for Science and Technology

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results.

关键词： Accuracy Magnetic resonance imaging Decision making Brain tumors Predictive models Brain modeling Data augmentation Data models Optimization Medical diagnostic imaging

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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An integrated machine learning and genetic algorithm approach for properties prediction of fly ash-based steel fiber-reinforced concrete

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Asian Journal of Civil Engineering 2024年第3期26卷 1175-1191页

作者： Keote, Rashmi Keote, Minal Balpande, Rupali S. Masram, Bharati Dubey, Pragati Pinjarkar, Latika Muley, Manjushree Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Computer Science and Applications Ramdeobaba University Maharashtra Nagpur 440013 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Mathematics S. B. Jain Institute of Technology Management and Research Maharashtra Nagpur 441501 India Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India

Enhancement of concrete strength is critically important for increasing construction materials’ lifespan and sustainability. Traditionally, concrete mixture optimization methods—especially those used for fly ash and steel fiber concretes—normally fail to accurately predict the strength due to the high degree of complexity and non-linearity involved in the interaction of their components. These limitations are overcome in this study, which uses advanced artificial intelligence techniques—The Multilayer Perceptron (MLP) Neural Networks, Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNN) to optimize concrete mixtures for improved strength. Among these, the MLP neural network was selected for this work because of its ability to model highly complex, nonlinear relationships and hence will be able to capture the intricate interactions among fly ash, steel fibers, and other additives. For this reason, Gradient Boosting Machine was chosen for its robustness against overfitting and high accuracy in handling linearity or nonlinearity in an optimization problem. Traditionally, CNN has been applied to image processing, but in this work, it had been uniquely adapted to include the spatial distribution of concrete mix components, hence giving a new dimension in strength prediction. In this study, every method was used with a comprehensive data set and the input variables were taken as the percentages of fly ash and steel fibers, the water-cement ratio, aggregate size distribution, and curing delays. The accuracies of prediction for the proposed models were improved significantly, with the Mean Absolute Error (MAE) for compressive strength by the MLP model and an R² value of 0.90–0.95 by the GBM model. It is interpreted from CNN that there could be a potential reduction in prediction error by 10–15% compared to traditional methods. The work provides a robust framework for concrete strength optimization with substantial improvements in the reliability and per

关键词： Concrete strength Fly ash Gradient boosting MLP neural networks Steel fibers Sustainable concrete

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A Novel Deep Learning Based Gender Classification, Expression Recognition and Emoticon Generation

A Novel Deep Learning Based Gender Classification, Expressio...

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Computing, Power and Communication Technologies (IC2PCT), IEEE international Conference on

作者： Bansidhar Joshi Bineet Kumar Joshi Sanjeev Kumar School of Computer Science Engineering and Applications D.Y. Patil International University Pune Himalayan School of Science & Technology Swami Rama Himalayan University Dehradun India ICFAI Tech School ICFAI University Dehradun India

Image is crucial in determining the gender and emotion of an individual in the digital age; however, the issue is evidently with the current methodologies. Implementing deep learning algorithms into image processing would be a more effective strategy. By developing emoticons from the emotions captured in images and snapshots, we hope to bridge the divide in communication. We implemented the Keras framework and evaluated its performance on Tensor Flow utilizing a CNN (Convolutional Neural Network) deep learning algorithm in order to ascertain gender. The objective is to eliminate noise and derive features from the image dataset in order to generate a new dataset that can be utilized to implement CNN. We have utilized the LSTM-RNN (long short-term memory recurrent neural network) to detect emotions and recognize facial expressions. With remarkable accuracy, the algorithm can determine the gender of an individual based on a live webcam feed or an image.

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE international Conference on computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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