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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Khatana, Gungun Rai, Praveen Kumar Shweta Dabral, Rini Gl Bajaj Institute Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798331519582

Brain cancer remains a major health challenge, particularly in constraint-driven settings like India, where it is the 10th leading cause of cancer-related deaths. Early detection is vital to improving treatment outcomes, yet traditional methods relying on manual MRI interpretation can be time taking, expensive, and highly susceptible to error. This study is presenting a deep learning based approach which is using Convolutional Neural Networks (CNNs). The proposed system uses advanced data preprocessing techniques, including Z-score normalization, skull stripping, and augmentation, to optimize MRI datasets. A robust CNN model was developed and trained on a diverse dataset comprising glioma, meningioma, pituitary, and non-tumor cases. This model achieved exceptional performance, with training, validation, and test accuracies of 99.95%, 99.8%, and 99.3%, respectively, along with high accuracy, recall, and F1 points. Evaluation metrics such as ROC-AUC and confusion matrix analysis showcased the model's accuracy in differentiating tumour types and stages. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Farming: An Integrated Platform for Crop and Fertilizer Prediction using Machine Learning and Orange Data Mining

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SN computer science 2025年第5期6卷 1-16页

作者： Abrol, Vipasha Gupta, Gaurav Pradhan, Soumya Ranjan GL Bajaj Institute of Technology and Management Noida India Department of Computer Science and Engineering Chandigarh University Chandigarh India

The purpose of this study is to examine the possible advantages of using machine learning algorithms into contemporary agriculture. These algorithms’ primary goal is to minimise waste and maximise agricultural yield by assisting in the making of well-informed decisions about crop planting, irrigation, and harvesting. The paper highlights the use of various supervised learning algorithms to address key challenges in agricultural management. Random Forest is employed for crop recommendation, while a combination of SVM and Random Forest tackles fertilizer recommendation. Our supervised learning models achieved high accuracy: Random Forest for crop recommendation (98.90%), SVM and Random Forest for fertilizer recommendation (97.89%).Top performers were SVM and Gradient Boosting, both achieving a result of 0.995 across all metrics through Orange Data Mining Tool. This paper contributes to the growing body of research exploring the applications of machine learning in agriculture. Specifically, it examines the effectiveness of different algorithms in tackling pest management challenges for various crops, offering a pathway towards cost-effective modernization of farming practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Crop management Gradient boosting Machine learning Orange SVM

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Enhanced Fake News Detection With Natural Language Processing

Enhanced Fake News Detection With Natural Language Processin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shrivastava, Sakshi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute Of Technology And Management Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350366846

The ubiquitous use of social media impacts the social life of humans. Fake news negatively affects a person's behavior. Fake news detection in social media has been a challenging problem for the last decade. Researchers are searching for accurate methods to detect fake news as early as possible. This paper discusses two NLP methods, LSTM, and bi-directional LSTM, that detect fake news. This paper uses a fake news challenge dataset, categorized into four stance labels: agree, disagree, discussed, and unrelated. Experimental results showed LSTM model received 92% accuracy in testing time and 95% accuracy in training time. On the extension of the method, Bi- directional LSTM achieved 95% accuracy in testing time and 98% accuracy in training time. © 2024 IEEE.

关键词： Natural language processing systems

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Lung Cancer Detection Using CNN Based Model 2

Lung Cancer Detection Using CNN Based Model

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Prince Nitin Kumar, Prince Rana, Naman Garg, Payal Gl Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Lung cancer can be fatal, but early diagnosis and treatment can save lives. Doctors use x-ray imaging to detect lung cancer, but it can be hard to distinguish cancerous cells from normal ones. computer-aided diagnosis can help doctors make accurate decisions. This research evaluates different computer-aided methods, analyzes the best one, identifies its flaws and limitations, and suggests a model with enhancements. The methods were ranked and compared based on their detection accuracy, and their flaws and limitations were examined. Some methods have low accuracy, while others have high accuracy, but not higher than 90%. The research aims to achieve an accuracy of 91 % or more © 2024 IEEE.

关键词： CNN Lung Cancer ResNet50 VGG16

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Deep Learning-Based Lung Cancer Detection Enhancing Early Diagnosis and Treatment Outcomes 2

Deep Learning-Based Lung Cancer Detection Enhancing Early Di...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Srivastava, Pragya Mishra, Asha Rani Chauhan, Sansar Singh Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350371055

As a result of late detection, treatment efficacy is limited, and survival rates are low, lung cancer continues to be a major global health concern. Convolutional Neural Network and pretrained convolutional neural networks (CNN) architectures - VGG-16, InceptionV3, and EfficientNetB0 which is based on Transfer learning is used for the task of lung cancer detection. The photos in the dataset are separated into test, validation, and training sets. During training, we use data augmentation approaches to make the models more resilient and broadly applicable. Model performance is evaluated using accuracy and loss metrics on the test set. The results demonstrate the effectiveness of the transfer learning based model, with EfficientNetB0 achieving the highest accuracy of 94% on the test set. This work demonstrates how deep learning models may be used to diagnose and classify chest CT scan pictures, which could lead to better lung cancer treatment and detection outcomes. © 2024 IEEE.

关键词： CNN CT scans Deep learning EfficientNetB0 InceptionV3 Lung cancer VGG-16

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Fake/Counterfeit Medical Product Identification Using Blockchain & QR Code 2

Fake/Counterfeit Medical Product Identification Using Blockc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Devanshu Singhal, Aryan Singh, Khushi Singh, Pushpa Gl Bajaj Institute of Technology and Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Counterfeiting in the field of medicine has raised substantial concerns as a global issue, with its prevalence noticeably expanding in recent years. Counterfeit medicines pose a particularly severe threat to patient's life or safety and cause severe implications. It is difficult to trace a medical product as well as to identify its authenticity. This paper aims to combat the widespread issue of counterfeit goods in industries like Pharmaceuticals and Healthcare by creating a Blockchain based product identification system. Combining the potential of Blockchain technology along with a secure QR code mechanism will offer robust security, immutability of data and have transparency in all the transactions. Thus, this system will ensure product traceability in the supply chain whereas the QR code will make it feasible for consumers to access product information instantly. © 2024 IEEE.

关键词： blockchain counterfeiting identification medicines QR code

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Anomaly Detection and Classification in Solar Panels Using Infrared Image and Deep Learning 2

Anomaly Detection and Classification in Solar Panels Using I...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Nagar, Sweety Rai, Praveen Kumar Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350371055

Maintaining the efficiency of solar panels is crucial for maximizing renewable energy generation. However, timely detection and addressing anomalies, such as hotspots or delamination, can be challenging. This research explores the potential of machine learning, specifically utilizing a ResNet-9 architecture with filter pruning, for anomaly detection in solar panels using infrared imagery. By analysing 20,000 labelled images from the Infrared Solar Modules dataset, the trained model achieved an accuracy of 80.2%. This research demonstrates the effectiveness of deep learning for solar panel anomaly detection, contributing to improved efficiency and sustainability of solar farms by enabling faster and more accurate anomaly identification. © 2024 IEEE.

关键词： anomaly detection infrared imagery Machine learning ResNet-9 architecture and Filter pruning Solar panel

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Enhancing Robustness of Deep Learning Model for Lung Cancer Detection: Adversarial Training and Gradient-based Interpretability

Enhancing Robustness of Deep Learning Model for Lung Cancer ...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Pragya Mishra, Asha Rani Chauhan, Sansar Singh Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management UP Greater Noida India

ISBN: (纸本)9798350355338

Lung cancer is a common and deadly type of cancer that is found universal. Identifying it early is crucial for improving patient results. Alternatively, it can be difficult because of the complexity of analysing radiological images. This research presents a robust deep learning model for detecting cancerous lesions in chest CT scan images. The model, using the ResNet50 architecture, experience training and evaluation using a dataset comprising interpreted chest CT scans obtained from Kaggle. The dataset was split into training, validation, and test sets, ensuring an equal distribution of cancerous and non-cancerous scans. After comprehensive training and validation, the model achieved an impressive accuracy of 99.68% in distinguishing between cancerous and non-cancerous images. Furthermore, the utilization of Grad-CAM visualization techniques has augmented the interpretability of the model, offering substantial discernment into the precise areas of interest within the CT scan images. Moreover, the model has shown flexibility in opposing adversarial attacks, thereby emphasizing its dependability in clinical environments. In summary, this study highlights the capacity of deep learning models to enhance diagnostic precision in healthcare, thereby potentially improving patient care and outcomes. © 2024 IEEE.

关键词： Lung cancer

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IoT and AI-based Intelligent Agriculture Framework for Crop Prediction

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International Journal of Sensors, Wireless Communications and Control 2023年第3期13卷 145-154页

作者： Singh, Pushpa Singh, Murari Kumar Singh, Narendra Chakraverti, Ashish Department of Computer Science & Engineering GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Management GL Bajaj Institute of Technology and Management Greater Noida India

Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Methods: Various supervised machine learning algorithms such as KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers have applied to build the proposed model. The model is continuously monitoring the field via various sensor data as a sample data for the prediction of best crops to be grown for farmers. Results: In this research, we investigated the contribution of supervised machine learning classifiers like KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers. The maximum accuracy has been observed as 99.39% of the Naïve Bayes classifier. Conclusion: In this paper an AI and IoT based model is used to recommend/predict the best crop based on environmental factors. The proposed model will collect the real time sensor data to predict the crops and plants smartly. © 2023 Bentham science Publishers.

关键词： Agriculture artificial intelligence blockchain crops ICT internet of things machine learning

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Sentiment Analysis of Movie Reviews Using Machine Learning

Sentiment Analysis of Movie Reviews Using Machine Learning

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Yadav, Avantika Garg, Payal Jain, Tarun Manipal University Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

This paper delves into the critical role that machine learning plays in the accurate classification of sentiments expressed in movie reviews. Throughout the study we research and apply NLP using deep learning and machine learning algorithms to train and test our IMDb 50K movie reviews dataset on different models and feature embedding to compare their accuracies and choose the most optimal model for sentimental analysis. Ultimately, using deep neural network model BERT neural gives us the best accuracy of 94.2% compared to lexicon-based approaches due to the limitations in feature extractions like Bag of Words and TF-IDF. © 2025 IEEE.

关键词： Bag of Words BERT Deep Learning IMDb Dataset Machine Learning Natural Language Processin Sentiment Analysis TF-IDF Training and Testing

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