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Machine Learning Framework for Women Safety Prediction using Decision Tree 5

Machine Learning Framework for Women Safety Prediction using...

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

作者： Sowmika, P. Sareena Rao, S.Siva Nageswara Rafi, Shaik Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

In every city, harassment and violence becomes one of the major problems for women. Further, women's personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women's safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations. © 2023 IEEE.

关键词： Decision trees

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Predictive Analytics in Agriculture: Unraveling the Determinants of Crop Yield with Machine Learning 26

Predictive Analytics in Agriculture: Unraveling the Determin...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sakib, Shadman Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Basak, Anindya Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In the context of Bangladesh, the precision in forecasting crop yields has been enhanced significantly through the integrative approach of amalgamating historical data, environmental variables, and cutting-edge technology. This integrative methodology underscores the fortification of the nation's agricultural domain, integral to both its economic fabric and the populace's sustenance. This study presents an integrative approach leveraging historical data, environmental variables, and advanced technology to enhance crop yield forecasting. Traditional agricultural methods, while valuable, often lack comprehensive understanding of climatic factors, limiting agricultural refinement. Employing an Advanced computer-Aided Decision Support System, this research meticulously analyzes extensive datasets to uncover intricate correlations between historical yields and environmental nuances. In this study, a novel interpretative machine learning model is formulated, emphasizing data pre-processing and optimal feature transformation protocols tailored to diverse model archetypes. The study delves into the influence of distinct variables on predictive acumen and the impact of individual observational values on forecast outcomes. The study's contributions include a transparent machine learning model elucidating key variables affecting crop yield predictions, paving the way for informed agricultural practices. Future endeavors could focus on leveraging real-time data and expanding datasets to further refine predictive models in this crucial domain. © 2023 IEEE.

关键词： Crop Yield Prediction Feature Importance Machine Learning Parameter Optimization

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Biopsy

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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Leveraging Deep Learning for Identification of Medicinal Plant Species 2

Leveraging Deep Learning for Identification of Medicinal Pla...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Vidya, H.A. Narasimha Murthy, M.S. Thara, D.K. KIT Tiptur Vtu Dept. of Computer Science and Engineering Belagavi India BMSIT&M Bengaluru Vtu Dept. of Information Science and Engineering Belagavi India CIT Gubbi Vtu Dept. of Information Science and Engineering Belagavi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373110

Identifying medicinal plant species is crucial in plant taxonomy because it allows us to harness the therapeutic properties of medicinal plants for human health. This information can be beneficial for the advancement of new drugs and treatments and for promoting the sustainable use of medicinal plants. Allopathic medicine has side effects in many cases. Traditional medicine, like Ayurveda, provides alternative treatments with minor side effects and helps protect human health. The decline of many medicinal plant species highlights the need for botanical research and biodiversity conservation. Artificial intelligence builds a gap between applications of computer technology in plant taxonomy. The goal of the study is to develop a reliable deep learning system for the recognition of medicinal plant species, which can subsequently aid in their preservation and support ongoing research efforts in this field. This paper focuses on a deep learning approach based on Convolutional Neural Network (CNN) to identify three distinct categories of medicinal plants. The model proposed uses CNN architecture and two pre-trained models, InceptionV3 and ResNet50, to classify three classes of medicinal plants, namely neem, peepal, and tulsi. The CNN model has an accuracy of 97.14% and a loss of 0.1274, the ResNet50 model has an accuracy of 97.06% and a loss of 0.1474, and the InceptionV3 model has an accuracy of 100% and a loss of 0.0199 in identifying the medicinal plant species. © 2024 IEEE.

关键词： Medicinal chemistry

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IRS Controlling Method With Machine Learning Based on Position Information 13

IRS Controlling Method With Machine Learning Based on Positi...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Data Movement in Heterogeneous Computing: A LASSA-based Approach for Efficient Nucleation List Precomputation 3

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Bhowmik, Biswajit Girish, K.K. Pandey, Himanshu Prabhanjans, Piyus National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 2025 IEEE.

关键词： Cellular Automata Models Heterogeneous Computing Nucleation List Precomputation OpenMP Performance Issues System Efficiency

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Game-Theoretic-Based Resource Allocation Algorithm for SCMA Max-Min Problem to Maximize Fairness 7

Game-Theoretic-Based Resource Allocation Algorithm for SCMA ...

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7th IEEE International Conference on Electronics and Communication engineering, ICECE 2024

作者： Cheraghy, Maryam Soltanpour, Meysam Abuhaija, Belal Abdalla, Hemn Barzan Ehimwenma, Kennedy Wenzhou-Kean University dept. Computer Science and Technology Wenzhou China Shandong University dept. Control Science and Engineering Jinan China

ISBN: (纸本)9798331517755

This paper tackles a joint optimization problem in uplink sparse code multiple access (SCMA) networks to enhance network fairness, i.e., Jain's fairness index (JFI). We propose a novel game-theoretic approach, the worst-case player first-strategy selection (WCPFSS) algorithm, to maximize the minimum achievable rate for all subcarriers while ensuring fair power allocation and optimized subcarrier assignment. This approach effectively manages user interference because each subcarrier acts as a player to exchange their strategies, which are clusters of users associated with each subcarrier. The simulation results confirm the superiority of the proposed WCPFSS method w.r.t. JFI for differential setup parameters. © 2024 IEEE.

关键词： Code division multiple access

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Development of IoT-based Scale Sensor to Measure AWD System Water Level in Crop Field 3

Development of IoT-based Scale Sensor to Measure AWD System ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Kabir, Md Shahriar Mahmud Niloy, Khalid Hossen, Md Imon Afrose, Sumaiya Hossain Mofazzol, Md. Ismail American International University-Bangladesh Dept. of Electrical & Electronics Engineering Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The paper introduces an innovative Internet of Things (IoT)-based scale sensor that measures the water level in the alternate wetting and drying (AWD) system and displays data in the mobile app to enable remote monitoring. The AWD system mainly consists of 30 cm of PVC pipe or bamboo with a diameter of 10-15 cm inserted into the paddy field, with a lower 20 cm buried in the soil and tiny holes at the bottom to allow field water to enter. The water level within the pipe indicates the field's water level, especially the underground water level. The AWD method optimizes water usage and reduces methane emissions by watering periodically and drying rice fields instead of maintaining continuous flooding. The current manual measurement process for water level needs to go to crop fields daily with a scale, which is difficult for continuous and remote monitoring. The developed scale sensor has IoT connectivity that removes the difficulties. The scale sensor works based on conductive rain sensors to precisely detect each centimeter rise or fall of water. The sensor consists of two open conductive layers that create a short circuit when the water level touches them by raising, triggering the microcontroller to register a voltage drop. The scale can measure up to 20 cm as it has 20 conductive layers, which fulfill the requirements. It has a 1 cm resolution, and in the test, it shows 95-99% accuracy. So, the sensor can effectively measure the water level in the AWD system, and further research is required to add an automatic irrigation system with the sensor so that the whole process can be controlled remotely. © 2024 IEEE.

关键词： Comparators (optical)

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Futuristics Technologies for Reduction of Plastic Waste 13

Futuristics Technologies for Reduction of Plastic Waste

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Dhiman, Riya Sharma, Ashish Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Electrical Engineering Mohali India

ISBN: (纸本)9798350380583

Plastic plays a vital role in daily life most of the food items, Baggies, vegetables are packed in plastic material. As plastic is non-biodegradable there are harming effect on human beings and animals. Due to these global warming also increases. Although 80% Fishes, 88% Vertebrates, 56% Mammals and 52% Reptiles are affected due to plastic waste. In this work with the various techniques proposed like Eco-bricks, Furniture, Replace Plastic By Bamboo and also Doormats are made through the Plastic Waste. It is seen that by using above techniques significantly reduce the harmful effect of plastic and there is improvement in life of human beings and animals also the alternative techniques to reuse the plastic and to replace plastic are proposed with which there is a increase in the earning of poor peoples. Above techniques are helpful to decrease the employment and also by using these techniques more than 60% of plastic reuse, with this the temperature is also decreased. © 2024 IEEE.

关键词： Invertebrates

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