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CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

Multimedia Tools and Applications 2025年第15期84卷 15501-15526页

作者： Agrawal, Rohit Singh, Navneet Pratap Shelke, Nitin Arvind Tripathi, Kuldeep Narayan Singh, Ranjeet Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science Race Course Road MP Gwalior474005 India School of AI Bennett University Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Bennett University TechZone 2 UP Greater Noida201310 India

Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better a

关键词： Discrete cosine transforms

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Octacleaner: Underwater Trash Detection Through YOLO 3

Octacleaner: Underwater Trash Detection Through YOLO

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Pavani, Dhanni Reddy, A Nymisha Nandini Saw, Neha Prasad, Siva Naik, Santosh Madeva Hyderabad Institute of Technology and Management Computer Science with AI & ML Hyderabad India Mechanical Engineering Hyderabad Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350317022

Million tons of trash is generated every day, which is often inadequately managed and sent to landfills. When it rains, trash along with agricultural waste and sediment, washes into water bodies, leading to underwater pollution. The heavy plastics, metals, glass, and organic waste settle at the water body's bottom, compounding the problem. To solve this,Octacleaner was developed which is an underwater trash-collecting robot equipped with a YOLO object detection model. In our study, the performance of YOLOv5 and YOLOv8 were compared. YOLOv5 boasts efficient and rapid object detection, making it suitable for real-time applications. It utilizes a streamlined architecture, making it computationally efficient while maintaining accuracy. YOLOv8, on the other hand, takes this a step further, achieving high precision of 90°/0, offering faster and highly accurate results. Its strength lies in its ability to swiftly and effectively identify objects. However, the unique challenge in underwater environments is that trash can change its shape due to water currents and decomposition. Additionally, factors like reduced sunlight and the presence of underwater mud can make the predictions more challenging. Despite this, YOLOv8 shows promise in addressing these difficulties, with its superior accuracy and speed. © 2023 IEEE.

关键词： Image Processing Object Detection Trash Underwater Pollution Underwater trash YOLOv5 YOLOv8

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Multilingual Transformer for Dynamic Cricket Commentary Generation

Multilingual Transformer for Dynamic Cricket Commentary Gene...

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Computing and Data science (ICCDS), International Conference on

作者： Gaurav Kamath Udayabhaskara N Department of Computer Science and Engineering (AI and ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The sport of Cricket is growing in popularity which necessitates the use of technology to adhere to popular game standards. Since this is a data rich and most elaborate game, understanding this for someone who's new to the game had been challenging. We are aiming to solve this unavailability of the game through native commentary to audience of all major languages. To outline our entire research work we ideate a Cricket Game ai through computer vision, use it to produce text commentary of game-play and perform Machine Translation(MT) to produce native commentary for a targeted language, As afirst part of the outlined process in this work we focus on the implementation of the machine translation component of this pipeline, evaluating various methods including Seq2seq and transformer based architectures. Our method of cross feeding Seq2Seq with Transformer model results in Bilingual Evaluation Understudy (BEL U) score on Validation set of 2.08.

关键词： Computational modeling Pipelines Games Data science Transformers Real-time systems Machine translation

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Intrusion Detection System in Wireless Sensor Networks using Modified Recurrent Neural Network with Long Short-Term Memory 2

Intrusion Detection System in Wireless Sensor Networks using...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ramkumar, K. Alzubaidi, Laith H. Malathy, V. Venkatesh, T. Kruthika, C.G. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Kpr Institute of Engineering and Technology Department of Ece Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350317558

Leveraging deep learning, Intrusion Detection Systems (IDS) for Wireless Sensor Networks (WSN) enhance security through advanced analytics, detecting and mitigating network intrusions in real-time for robust protection. Evaluations and scalability considerations are critical for improving the applicability and robustness of intrusion detection systems based on deep learning techniques. This paper introduces a novel approach: a modified Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) for IDS in WSN classification, utilizing the NSL-KDD dataset. Rigorous performance validation demonstrates outstanding metrics, including an accuracy of 99.95%, precision at 99.93%, and a robust recall of 95.69%, with the F1-score reaching 99.80%, surpassing benchmarks set by existing methods. Notably, current approaches like Deep Neural Network (DNN), Convolutional Neural Network-LSTM (CNN-LSTM), Conditional Generative Adversarial Network (CGAN), and Stacked Autoencoders-DNN (SAE-DNN) face limitations in achieving optimal accuracy and robustness. The proposed RNN-LSTM method exhibits significant advancements in addressing these challenges for effective intrusion detection in WSN © 2024 IEEE.

关键词： Convolutional Neural Network Deep Neural Network Intrusion Detection System Long Short-Term Memory Wireless Sensor Network

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Malware Detection Using the Machine Learning Based Modified Partial Swarm Optimization Approach 1

Malware Detection Using the Machine Learning Based Modified ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Sivakumar, S. Saminathan, S. Ranjana, R. Mohan, M. Pareek, Piyush Kumar Department of Computer Science Perambalur India Srinivasan College of Arts and Science Department of Computer Science and IT Perambalur India Panimalar Engineering College ECE Department India AP/CSE KPR Institute of Engineering and Technology India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Malware analysis includes a crucial step after malware detection called malware categorization, which classifies dangerous files. There have been many reported static and dynamic methods for classifying malware up to this point. The ml-MD strategy presented in this study uses static methods to categorise various malware families and is based on machine learning. In order to detect malware, we create a new machine learning-based framework. The characteristics from the dataset are extracted in this case using principal component analysis (PCA). In order to offer the best malware detection solutions, introduce a machine learning-based Modified Particle Swarm Optimization (MPSO) algorithm. Improved Accuracy and detection rate using ml-based MPSO technique. The effectiveness of the suggested technique in detecting malware is demonstrated by the experimental results on several benchmark data sets, which greatly outperform alternative approaches. © 2023 IEEE.

关键词： Machine learning

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Ebin, P.M. Thomas, Roja Ismail, Safad Kavitha Nair, R. Alliance School of Advance Computing Alliance University Banglore India Mangalam College of Engineering Department of Computer Science and Engineering Ettumanoor India College of Engineering Chengannur Department of Computer Engineering Chengannur India Acharya Institute of Technology Department of Ai &ml Banglore India

ISBN: (纸本)9798350313796

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques. © 2023 IEEE.

关键词： Tumors

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Enhancing Customer Retention through Feature Selection and XGBoost Classification for Churn Prediction

Enhancing Customer Retention through Feature Selection and X...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Raye Haarika Ayain John Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Customer churn remains a significant and pressing challenge for both large enterprises and businesses across various industries. Particularly within the banking sector, where revenue is directly influenced by customer retention, there is a concerted effort to enhance predictive methods for identifying potential churn. This paper explores different machine learning (ml) algorithms employed in constructing a churn model to assist banking operators in predicting customers at risk of churning using minimum attributes and aiming to achieve good accuracy. The research delves into experimental results to compare the efficacy of various techniques, identifying the model that works best for predicting customer attrition in the banking sector in the end. This study uses feature selection and the XGBoost algorithm to select attributes based on their ranks.

关键词： Training Industries Accuracy Costs Computational modeling Machine learning Banking

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A Comparative Study of Machine Learning Models for Early Stage Identification of Powdery Mildew on Cherry Leaf

A Comparative Study of Machine Learning Models for Early Sta...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Supriya Shukla, Ashutosh Manchanda, Mahesh Coer University Department of Ai and Ml Roorkee India Upes University Electrical Cluster Dehradun India Graphic Era Hill University Department of Computer Science and Engg Dehradun India

ISBN: (纸本)9798350382723

Agriculture is the primary source of assistance for all the key sections of our country, INDIA. Population growth and Urbanization are the two major factors that create serious challenges for this field. According to the report of the Food and Agriculture Organization of the United Nations, by the next two decades, there will be a need for IT infrastructure to handle the rapidly expanding dietary needs of the two billion human population. Precision agriculture is the smart aid to resolving these issues. it allows for more efficient use of resources. It helps farmers to meet the growing demand for food sustainably by implementing smart IT techniques for several activities like Site-specific planting and fertilization, Crop monitoring, pest management, and variable rate irrigation. Autonomous vehicles, etc. keeping this in view a comparative study on advanced machine learning. Support vector machine, K-Nearest Neighbor, Artificial Neural Network, and Decision Tree algorithms have been implemented on MATLAB TOOL for early-stage identification of powdery mildew on cherry leaf. Hereafter considering various performance metrics like Confusion metrics parameters (accuracy, recall, precision), and ROC & AUC curve, it is found that ANN provides the best results of 91% on 1,200 images of the standard open source repository. © 2024 IEEE.

关键词： Decision trees

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SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHAVIOR 12

SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Chen, Huayu Lu, Cheng Wang, Zhengyi Su, Hang Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Guangdong Guangzhou China

Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies. However, sampling from diffusion policies is considerably slow because it necessitates tens to hundreds of iterative inference steps for one action. To address this issue, we propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization. Our method enjoys powerful generative capabilities of diffusion modeling while completely circumventing the computationally intensive and time-consuming diffusion sampling scheme, both during training and evaluation. Extensive results on D4RL tasks show that our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks, while still maintaining state-of-the-art performance. Code: https://***/thu-ml/SRPO. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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