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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Balakrishnan, Minu Raja, M. Kumar, P.Vinoth Subramaniam, Suvitha Latha, R. Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Chennai Institute of Technology Depatment of Mechatronics Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350322842

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%. © 2023 IEEE.

关键词： Convolutional neural networks

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Intelligent Movie Recommender based on Emotions

Intelligent Movie Recommender based on Emotions

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Archana Naik P Ushashree Isha Bharadwaj Archita Bhatnagar Divya Potula Fouziya Department of Computer Science and Engnieering Nitte Meenskshi Institue of Technology Bangalore India Depatment of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India

Recommender systems are designed to help users navigate through a vast space of possible options and find the best ones that meet their needs and preferences. User state of mind and emotions are crucial for making an appropriate decision for personalization of products like music, news, and movies. In these cases, user emotions can strongly influence their overall experience and satisfaction, often more so than objective factors like plot, genre, or ratings. Recognizing the importance of user emotions, modern recommender systems have begun incorporating emotional analysis techniques into their algorithms. These techniques can help the system better understand and capture the emotional states of users, which in turn allows for more accurate and personalized recommendations.

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Wideband Tunable Transmission Type Polarization Converter Metasurface using Graphene for Terahertz Frequency Regime

Wideband Tunable Transmission Type Polarization Converter Me...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Hiranmay Mistri Anumoy Ghosh Abdur Rahaman Sardar Department of Electronics and Communication Engineering Ramkrishna Mahato Government Engineering College Purulia India Department of Electronics and Communication Engineering National Institute of Technology Mizoram Aizwal India Depatment of Computer Science and Engineering Ramkrishna Mahato Government Engineering College Purulia India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

This article represents a broadband transmission type linear-to-circular polarization converter (LTCPC) realized using a graphene metasurface for the terahertz (THz) frequency regime. The composition of the unit cell includes a lossy-SiO2 substrate with a graphene-based sub-wavelength mono-layer structure on the top and a similar but very thin gold sub-wavelength structure at the back side to cover it partially. This metasurface can be used to transform waves with linear polarization into circularly polarized waves from 9.15 THz to 13.0 THz, i.e., about 3.85 THz bandwidth and about 35 percent of fractional bandwidth (FBW). The chemical potential (CP) and relaxation time (RT) can be varied to tune the graphene metasurface over a vast range of frequencies. All physical measurements of the unit cell geometry are optimized for performance optimization. The proposed lumped parameter circuit for the unit cell is also incorporated. The proposed unit cell can perform invariably up to 40° incident angle.

关键词： Wireless communication Time-frequency analysis Graphene Transforms Metasurfaces Frequency conversion Wideband Optimization Substrates Terahertz communications

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Classification of diabetes by kernel based SVM with PSO

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第4期14卷 1242-1255页

作者： Choubey, Dilip K. Tripathi, Sudhakar Kumar, Prabhat Shukla, Vaibhav Dhandhania, Vinay K. Department of Computer Science & Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Information Technology Rajkiya Engineering College Ambedkar Nagar India Depatment of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Tech Mahindra Mumbai Maharasthra India Department of Diabetology Bombay Medical Hall Upper Bazar Ranchi India

Background: The classification method is required to deduce possible errors and assist the doctors. These methods are used to take suitable decisions in real world applications. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Methods: The proposed methodology comprises two phases: The first phase deals with t h e description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset, whereas in the second phase, the dataset has been processed through two different approaches. Results: The first approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO has been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: The present work consists of a comparative analysis of outcomes w.r.t. performance assessment has been done PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO selects the relevant features, reduces the expense and computation time while improving the ROC and accuracy. The used methodology could be implemented in other medical diseases. © 2021 Bentham science Publishers.

关键词： Classification (of information)

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Epidermis Bruise Analysis Using Deep Learning

Epidermis Bruise Analysis Using Deep Learning

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Vansheta Sharma Rupa Rani Priya Pathak Sudeshna Chakraborty Department of Computer Science & Engineering- Data Science ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan Department School of Computer Application and Technology Galgotias University UP India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model’s suitability for medical applications.

关键词： Deep learning Analytical models Accuracy Biological system modeling computer architecture Feature extraction Skin

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Redefining Disease Assessment in Raspberries: A Hybrid GAN-SVM Model for Detailed Intensity Measurement

Redefining Disease Assessment in Raspberries: A Hybrid GAN-S...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Dinesh Arora Deepika Sharma Avinash Sharma Parul Goyal Department of Electronics & Communications Engineering Chandigarh Enigineering College Landran Mohali Punjab India Depatment of Biotechnolgy Chandigarh College of Technology Landran Mohali Punjab India Department of Computer Science & Engineering Chandigarh Enigineering College Jhanjeri Mohali Punjab India Computer Science & Engineering Department M. M Engineering College Maharishi Markandeshwar Deemed to be University Mullana Ambala Haryana India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This study presents a hybrid Generative Adversarial Network (GAN) and Support Vector Machine (SVM) model for accurately categorizing Raspberry Cane Disease (RCD) into four severity levels. Leveraging a dataset of 7,000 high-resolution images, the model achieves 98.8% accuracy. GANs generate synthetic images to address data scarcity, while SVMs ensure precise multi-class classification. The results demonstrate the model's effectiveness in disease severity assessment, highlighting its potential for broader agricultural applications.

关键词： Support vector machines Precision agriculture Plant diseases Accuracy Food security Generative adversarial networks Ubiquitous computing Vectors Hybrid power systems Information systems

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Integrating CNN and Genetic Algorithms for Kohlrabi Variety Classification in India: A Study of Purple Vienna, White Vienna, and Grand Duke

Integrating CNN and Genetic Algorithms for Kohlrabi Variety ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Nadim Akhtar Ruchi Singla Dinesh Arora Deepika Sharma Avinash Sharma Department of Computer Science Engineering Chandigarh Group of Colleges Landran (Mohali) Punjab India Director Research & Devlopment Chandigarh Group of Colleges Landran (Mohali) Punjab India Department of Electronics & Communications Engineering Chandigarh Enigineering College Landran Mohali Punjab India Depatment of Biotechnolgy Chandigarh College of Technology Landran Mohali Punjab India Department of Computer Science & Engineering Chandigarh Enigineering College Jhanjeri Mohali Punjab India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The proposed work looks into differences between types of Kohlrabi common in India using special systems called Convolutional Neural Networks (CNN) with Genetic Algorithms (GA). A set of 4000 high-quality pictures was carefully gathered and readied. These included Purple Vienna, White Vienna, and Grand Duke types. The CNN design was made to find small differences in pictures. It used convolutional, pooling, and fully connected parts for a strong grouping of things. The CNN model was good at telling apart different kinds of kohlrabi. It got it right 96.12 % of the time based on how they looked and what color they were! The test results showed that the model was very accurate across all categories, showing it is reliable in spotting small differences. Comparing it to other ways of doing things, found that the CNN model worked better. This made sure it is a strong and trustworthy choice for telling different types of Kohlrabi apart. This study shows that machine learning methods, especially CNNs can be good for farming tasks. It gives hope to finding the right type of plants better in farm work and separate markets for sales. Also, these techniques help improve breeding programs. The good use of CNNs in correctly grouping Kohlrabi types shows better ways to grow crops and can help with making food long-lasting. It also leads people closer to understanding what different plants look like, helping those who work on farms make smarter choices.

关键词： Training Accuracy Shape Neural networks Crops Convolutional neural networks Reliability Farming Genetic algorithms Sorting

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Consumer Behavior and Satisfaction Rate in Social Media Purchasing Ads Using Radial Basis Function Networks (RBFNS) Based BAT Algorithm 3

Consumer Behavior and Satisfaction Rate in Social Media Purc...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Lilly Florence, M. Varun, T. Senthilkumar, S. Florence, N. Sheeba Seal, Sudeshna Sangeetha, S. Adhiyamaan College of Engineering Tamlinadu India Anna University Department of Management Studies TamilNadu Chennai India Kalasalingam Academy of Research and Education Tamilnadu India Sona College of Technology Department of Mathematics Tamil Nadu Salem India OmDayal Group of Institutions West Bengal Howrah India Velammal College of Engineering and Technology Depatment of Computer Science and Engineering TamilNadu Madurai India

ISBN: (纸本)9798331542375

Companies who wish to enhance their marketing plans must first be quite conscious of consumer behaviour and satisfaction with social media campaigns allowing purchase capability. Many unstructured data generated by social media channels makes it challenging to find feasible application concepts. Usually, the conventional approaches do not sufficiently show the non-linear interactions between consumer reactions and advertisement effectiveness. While current machine learning methods such artificial neural networks (ANN) or Support Vector machines (SVM) can offer insights, they lack the optimising force required to properly fine-tune parameters. The forecasts of satisfaction rates thus differ from ideal. Better predictive accuracy is obtained in the technique by using a Bat Algorithm in concert with Radial Basis Function Networks (RBFNs). The Bat Algorithm maximises RBFN parameters, which include weights, spread, and centers, so improving their performance. Ten thousand consumer records from social media buying campaigns made up the data used for the study. Among the data were click-through rates and degrees of satisfaction. For model validation as well as training, an 80-20 split was used. Predicting consumer satisfaction with a 96.2% accuracy, the RBFN-Bat model outperformed already in use approaches including artificial neural networks (92.5%) and support vector machines (89.7%). Moreover, the mean absolute error (MAE) dropped to 0.045 and the computational efficiency rose by 18% in comparison to earlier applied techniques. © 2025 IEEE.

关键词： Consumer behavior

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Consumer Behavior and Satisfaction Rate in Social Media Purchasing Ads Using Radial Basis Function Networks (RBFNS) Based BAT Algorithm

Consumer Behavior and Satisfaction Rate in Social Media Purc...

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Automation and Computation (AUTOCOM), International Conference on

作者： Lilly Florence M T. Varun S. Senthilkumar N. Sheeba Florence Sudeshna Seal Sangeetha S Adhiyamaan College of Engineering Tamlinadu India Department of Management Studies Anna University Chennai Tamilnadu India Kalasalingam Academy of Research and Education Tamilnadu India Department of Mathematics Sona College of Technology Salem Tamil Nadu India OmDayal Group of Institutions Howrah West Bengal India Depatment of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Companies who wish to enhance their marketing plans must first be quite conscious of consumer behaviour and satisfaction with social media campaigns allowing purchase capability. Many unstructured data generated by social media channels makes it challenging to find feasible application concepts. Usually, the conventional approaches do not sufficiently show the non-linear interactions between consumer reactions and advertisement effectiveness. While current machine learning methods such artificial neural networks (ANN) or Support Vector machines (SVM) can offer insights, they lack the optimising force required to properly fine-tune parameters. The forecasts of satisfaction rates thus differ from ideal. Better predictive accuracy is obtained in the technique by using a Bat Algorithm in concert with Radial Basis Function Networks (RBFNs). The Bat Algorithm maximises RBFN parameters, which include weights, spread, and centers, so improving their performance. Ten thousand consumer records from social media buying campaigns made up the data used for the study. Among the data were click-through rates and degrees of satisfaction. For model validation as well as training, an 80–20 split was used. Predicting consumer satisfaction with a 96.2% accuracy, the RBFN-Bat model outperformed already in use approaches including artificial neural networks (92.5%) and support vector machines (89.7%). Moreover, the mean absolute error (MAE) dropped to 0.045 and the computational efficiency rose by 18% in comparison to earlier applied techniques.

关键词： Support vector machines Accuracy Consumer behavior Social networking (online) Heuristic algorithms Computational modeling Artificial neural networks Radial basis function networks Prediction algorithms Optimization

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Machine Learning for Cardiac Disease Detection and Family History Association

Machine Learning for Cardiac Disease Detection and Family Hi...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Inumarthi V. Srinivas G. Saritha Renu Yadav S. Subburam K. Divya Data Science and Technology Depatment KJ Somaiya Institute of Management Mumbai India Computer Science Sri Sairam Institute of Technology India Department of Electronics & Communication Engineering IES College of Technology Bhopal India New Prince Shri Bhavani College of Engineering and Technology India Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

As cardiac diseases continue to rise, early detection is crucial for effective treatment. Machine learning (ML) offers promising avenues for accurate diagnosis. This study explores the integration of family history, a vital risk factor, into ML-based cardiac disease detection models. By leveraging diverse patient datasets, ML algorithms are trained and validated. Incorporating family history enhances predictive accuracy. The literature review highlights the significance of genetics in disease predisposition. Model performance metrics demonstrate improved precision, recall, and overall accuracy. Interpretation of results reveals the interplay between genetics and ML. While data limitations and biases are acknowledged, the study presents implications for both clinical practice and research. This research contributes to the growing field of ML-driven medical diagnostics and underscores the importance of family history in cardiac disease prediction.

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