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SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

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Stock Price Prediction using Modified BPSO for Feature Selection with RNN Variants on Top Tech Companies

Stock Price Prediction using Modified BPSO for Feature Selec...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Gupta, Priyank Paharia, Nitin Gupta, Sanjay Kumar Jadon, Rakesh Singh Jiwaji University SOS Computer Science and Applications Gwalior India Prestige Institute of Management and Research Department of Computer Applications Gwalior India MITS Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350311204

The most often utilized data in recent years is time series data, used for things like stock market price prediction and others. Recent research has focused chiefly on stock market prediction using neural networks, and new techniques are being developed to improve these models' accuracy. One such method uses optimization algorithms like PSO, GWO, or GA. Other variants, such as Binary-PSO and Binary-GWO, are developed by further enhancing the original algorithms. The paper suggested a variant of BPSO called MBPSO / TBPSO that assigned binary values to the features during optimization using the 'tanh' activation function. The TBPSO is then applied individually for two, five, and ten years to the datasets of five leading Tech firms. The dataset, which comprises a variety of historical and technical indicators, is applied to different variants of RNN and finds the best results with the GRU model to forecast the closing stock price. What transpired proves that, when used for prediction on the GRU model and when choosing more features than BPSO, TBPSO provides us with features that will provide more accuracy. © 2023 IEEE.

关键词： Forecasting

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network 6

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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A Hybrid Stacked Ensemble Technique to Improve Classification Accuracy for Neurological Disorder Detection on Reddit posts 14

A Hybrid Stacked Ensemble Technique to Improve Classificatio...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Garg, Tejaswita Gupta, Sanjay Kumar School of Studies in Computer Science & Applications Jiwaji University Gwalior India

ISBN: (纸本)9781665487719

Sentiment analysis helps in the early detection of depression as identify unpleasant mental states in people who are at risk for developing mental disorders. By utilizing both syntactic and semantic information, modelling approaches for sentiment analysis rely on machine learning algorithms. In this paper, a hybrid stacked ensemble learning approach has been used for the detection of depression as a neurological disorder. With the help of pre-trained word embeddings, the Word2Vec, GloVe and Fasttext are chosen for data preprocessing and feature extraction. Then, to identify depressed and non depressed identities, we integrate a hybrid stacked ensemble learning approach over Random Forest (RF), Support vector machines (SVM), K-Nearest Neighbor (KNN) and Catboost classifier (CBC) as base models whereas logistic regression (LR) as meta model classifier. The results of the experiments show that suggested model performs best with our proposed model than individual models. It is also found that with Word2Vec word embedding model, the proposed model achieved the higher accuracy as 99% in comparison to GloVe and Fasttext that categorizes depressed over non depressed users on the social media platforms. © 2022 IEEE.

关键词： Sentiment analysis

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Efficient Approaches to Predict Neurological Disorder using Social Networking Sites 11

Efficient Approaches to Predict Neurological Disorder using ...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Garg, Tejaswita Gupta, Sanjay Kumar Jiwaji University School of Studies in Computer Science and Applications Gwalior India

ISBN: (纸本)9781665480383

With the extensive utilization of Social Networking Sites (SNS), users tend to disclose their feelings through various posts, images, emoticons posted over SNS profiles. Here, different information diagnostic ways are gently analyzed to predict neurological disorder through cognitive-behavioral approach, sentiment analysis, and machine learning techniques using earlier publications. It is observed that Machine learning approach gives higher accuracy and better results than Cognitive behavioral therapy and Sentiment Analysis. Also, it is being noticed that the techniques like Support Vector machine (SVM), K Fold validation, Logistic Regression, Decision Tree, and Random Forest are mostly used for the prediction of emotional disclosure. It is being noticed that depression is most common mental illness found among individuals along with some signs of Stress, anxiety, bipolar disorder or even suicidal ideation. However, all techniques are more versatile and robust but SVM works out the best amongst all. © 2022 IEEE.

关键词： Sentiment analysis

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Stock Price Prediction using Modified BPSO for Feature Selection with RNN Variants on Top Tech Companies

Stock Price Prediction using Modified BPSO for Feature Selec...

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Communication & Computing (WCONF), World Conference on

作者： Priyank Gupta Nitin Paharia Sanjay Kumar Gupta Rakesh Singh Jadon SOS Computer Science and Applications Jiwaji University Gwalior India Department of Computer Applications Prestige Institute of Management and Research Gwalior India Department of Computer Science and Engineering MITS Gwalior India

The most often utilized data in recent years is time series data, used for things like stock market price prediction and others. Recent research has focused chiefly on stock market prediction using neural networks, and new techniques are being developed to improve these models' accuracy. One such method uses optimization algorithms like PSO, GWO, or GA. Other variants, such as Binary-PSO and Binary-GWO, are developed by further enhancing the original algorithms. The paper suggested a variant of BPSO called MBPSO / TBPSO that assigned binary values to the features during optimization using the “tanh” activation function. The TBPSO is then applied individually for two, five, and ten years to the datasets of five leading Tech firms. The dataset, which comprises a variety of historical and technical indicators, is applied to different variants of RNN and finds the best results with the GRU model to forecast the closing stock price. What transpired proves that, when used for prediction on the GRU model and when choosing more features than BPSO, TBPSO provides us with features that will provide more accuracy.

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Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging 4

Early Cancer Detection and Diagnosis using Deep Learning Mod...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Kumar, Sharma Sonu Monalisha, Mitali Satapathy, Lopamudra Nisha, M. Jiwaji University Dept. of Computer Science Pgv College Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Paramedical Science Ranchi India Usha Martin University Dept. of Agriculture Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types - benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques. © 2024 IEEE.

关键词： Lung cancer

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Enhancing SMS Spam Detection Using Deep Learning Models: A Comparative Study 1

Enhancing SMS Spam Detection Using Deep Learning Models: A C...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Sikarwar, Shailendra Arivukkodi, R. Krishnane, Dhanya Sharma, Himanshu Namdeo, Avinash Jadon, Kaushiki Jiwaji University Dept. of Computer Science Gwalior India Meenakshi A.H. Educ. & Research Dept. of Computer Applications Chennai India Dhanalakshmi Srinivasan Dept. of CSE Coimbatore India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Mechanical Engineering Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

The rise in Machine Learning algorithms for identifying SMS spam has become popular due to a significant surge in unwanted text messages. Detecting SMS spam holds crucial importance for several reasons. Firstly, it can inundate mobile phone users' message inboxes with irrelevant and unwanted messages, causing frustration and irritation. Secondly, SMS spam serves as a conduit for phishing scams, where scammers exploit fraudulent SMS messages to deceive users into divulging personal information or downloading malicious software. These scams can result in financial losses, identity theft, or other forms of fraud. Lastly, SMS spam can propagate malware or viruses, potentially compromising the security or functionality of the user's device. © 2024 IEEE.

关键词： Phishing

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Satish Bansal Rakesh S Jadon Sanjay Kumar Gupta Prestige Institute of Management & Research Gwalior India Department of Computer Engineering MITS Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

关键词： Deep learning Industries Tongue Lips Computational modeling Image processing Neural networks

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Data Mining Techniques in Financial Fraud Detection 1

Data Mining Techniques in Financial Fraud Detection

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Sharma, Yash Ambika, P. Upadhyay, Amit Kumar Mahajan, Samriti Kumar, Rupendra Jiwaji University Dept. of Mathematics Gwalior India CSE-APEX Chandigarh India Meenakshi A.H.E.R Dept. of Commerce Chennai India Mangalayatan University Dept. of Computer Engineering & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Commerce & Management Faridabad India JS University Dept. Computer Science Shikohabad India

ISBN: (纸本)9798350356816

In today's world, financial fraud poses a real threat to global economies, necessitating robust detection systems. Utilizing data mining methods in fraud detection has emerged as a new approach, offering powerful tools, especially with large financial transaction datasets. This paper explores various Data Mining techniques applied in Financial Fraud Detection, including decision trees, neural networks, and clustering methods, highlighting their effectiveness in identifying fraud. As decision trees can easily classify & predict transactions based on this historical data, it helps to recognize fraudulent activities. Neural networks can improve the accuracy of predicting fraudulent transactions by detecting intricate patterns, they are capable & adaptable for learning even odd more. Data points that are close together with each other end up in the same cluster - revealing outliers such as fraud;which would appear to be a point very distant from its neighbours. The adoption of these methodologies within financial systems prevents fraud before a crime has been committed, allowing for the resources & time spent on manual investigation to be reallocated. In addition, the development of machine-learning solutions & further enhancements in artificial intelligence allows for specialized fraud prevention methods to evolve. This abstract emphasises the importance of data mining for financial security which also points at some on-going research in this area. © 2024 IEEE.

关键词： Support vector machines

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