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Your search keyword '"Sinwar, Deepak"' showing total 11 results
Start Over Author "Sinwar, Deepak" Topic machine learning
11 results on '"Sinwar, Deepak"'

1. Stock Recommendations Using Machine Learning and Natural Language Processing

Author

Sinha, Akruti, Anup, Mahin, Sinwar, Deepak, Kumar, Ashish, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Kartik, S., editor

Published
2024
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3. Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.

Author

Bhakar, Suman, Sinwar, Deepak, Pradhan, Nitesh, Dhaka, Vijaypal Singh, Cherrez-Ojeda, Ivan, Parveen, Amna, and Hassan, Muhammad Umair

Subjects
*ALZHEIMER'S disease, *PARKINSON'S disease, *DIAGNOSIS, *AUTONOMIC nervous system, *DIABETIC retinopathy, *KNEE pain, *DYSAUTONOMIA
Abstract

Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches. [ABSTRACT FROM AUTHOR]

Published
2023
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4. IoT-Based Real-Time Crop and Fertilizer Prediction for Precision Farming 4.0

Author

Tiwari, Shailendra, Garg, Ravit, Kushal, N., Khurana, Manju, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Dey, Nilanjan, editor, Perumal, Thinagaran, editor, and Tavares, João Manuel R. S., editor

Published
2023
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5. Crop Recommendation for Maximizing Crop Yield Using Random Forest

Author

Kumar, Amit, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Dey, Nilanjan, editor, Perumal, Thinagaran, editor, and Tavares, João Manuel R. S., editor

Published
2023
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6. Combination of Expression Data and Predictive Modelling for Polycystic Ovary Disease and Assessing Risk of Infertility Using Machine Learning Techniques

Author

Vats, Sakshi, Sengupta, Abhishek, Chaurasia, Ankur, Narad, Priyanka, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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7. Modeling Daily Pan Evaporation Using Tree-Based Regression Methods

Author

Babu, Sherin, Thomas, Binu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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8. Political Polarity Classification Using NLP

Author

Varma, Sagi Harshad, Harsha, Yanamandra Venkata Sree, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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9. A Comprehensive Source Code Plagiarism Detection Software

Author

Jain, Amay Dilip, Gupta, Ankur, Choudhary, Diksha, Nayan, Tiwari, Ashish, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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10. Lung Cancer Detection by Classifying CT Scan Images Using Grey Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbours

Author

Kamdar, Aayush, Sharma, Vihaan, Sonawane, Sagar, Patil, Nikita, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Perumal, Thinagaran, editor, Slowik, Adam, editor, and Tavares, João Manuel R. S., editor

Published
2022
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11. A framework for detection of cyber attacks by the classification of intrusion detection datasets.

Author

Srivastava, Durgesh, Singh, Rajeshwar, Chakraborty, Chinmay, Maakar, Sunil Kr., Makkar, Aaisha, and Sinwar, Deepak

Subjects
*INTRUSION detection systems (Computer security), *CYBERTERRORISM, *SUPPORT vector machines, *K-nearest neighbor classification, *VECTOR data, *SOFT computing, *MACHINE learning
Abstract

Recognition of the consequence for advanced tools and techniques to secure the network infrastructure from the security risks has prompted the advancement of many machine learning-based intrusion detection strategies. However, it is a big challenge for the researchers to make improvements in an Intrusion Detection System with desired advantages and constraints. This paper has developed a proficient soft computing framework using Grey Wolf Optimization and Entropy-Based Graph (GWO-EBG) to classify intrusion detection datasets to reduce the false rate. In the proposed scheme, initially, the input data is preprocessed by the data transformation and normalization procedure. After the preprocessing, optimal features have been chosen for the dimension reduction from the preprocessed data using the grey wolf optimization (GWO) algorithm. Then, the Entropy value has estimated from the idyllically selected features. Lastly, an Entropy-Based Graph (EBG) has been constructed to classify data into intrusion or normal data. The experimental results demonstrate that the developed method outperforms other existing methods in various performance measures. The detection rate of the developed GWO-EBG is found to be 94.6%, which is higher than 91.24 % of EBG, 75.60 % K-Nearest Neighbors (KNN), 73.36 % of Support Vector Machine (SVM), and 74.88 % of Generalized Regression Neural Network (GRNN) on 5000 connection vectors data obtained from KDD CUP'99 testing dataset. The false-positive rate of developed strategy (GWO-EBG) is 0.35 %%, which is lower than 2.18 % of EBG, 7.32 % KNN, 8.15 % of SVM, and 8.13 % of GRNN with 5000 testing datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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