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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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Real Time Machinery Health Detection Via Thermal Imaging 10

Real Time Machinery Health Detection Via Thermal Imaging

引用

10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Khan, Jahiya Nene, Manisha J Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798331511890

Marine vessels are equipped with advanced and integrated complex machines that operate in challenging conditions, where the failure rate is high. One of the most vital elements of these machines is fault diagnosis and maintaining 100 percent operational states. Any machinery breakdown at sea during maritime operations can have severe consequences leading to operational impacts and reducing combat efficiency. The existing maintenance routines - PPM (Planned Preventive Maintenance) and CBPM (Condition Based Predictive Maintenance) are reactive approaches that may miss early signs of material degradation, leading to unplanned machinery breakdown at sea. An efficient method to counter this issue in real time is thermal imaging. The dataset required to develop a thermal prognostic model for real-time machinery health detection is not available. This paper presents a systematic approach to develop a prognostic model for real time machinery health detection via thermal imaging. We have developed a custom thermal image dataset representing various machinery conditions-normal, defective and degraded. We have developed our real-time prognostic model 'Thermal Prognostic Model'. This model targets fuel systems to prevent major fires and accidents at sea by predicting potential failures and taking corrective actions with mAP50 0.956, precision 0.945 and recall 0.957. © 2025 IEEE.

关键词： Predictive maintenance

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Frank, Anilloy Raj, Mrinal Presidency University Dept. of Electronics and Communication Engineering Bangalore India Presidency University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350309140

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation. © 2024 IEEE.

关键词： Child Emotional development face recognition Image processing Machine Learning in education

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Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection system 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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Multiclass Brain Tumor Detection using Deep Transfer Learning

Multiclass Brain Tumor Detection using Deep Transfer Learnin...

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Deshmukh, Aishwarya V. Bendre, M.R. Prawara Rural Engineering College Dept. of Data Science Engineering Loni India Prawara Rural Engineering College Dept. of Computer Engineering Loni India

ISBN: (纸本)9798350362879

Brain tumors, identified as abnormal growth of cell inside the brain, necessitate accurate categorization for tailored treatment plans. This classification aids in determining the severity of the condition and guiding therapeutic decisions. Iimaging technologies in medical like Magnetic Resonance Imaging (MRI) are instrumental in identifying brain pathologies without emitting ionizing radiation. Deep learning, a subset of AI, enhances brain tumor detection from MRI scans, improving prediction rates. Digital image processing is vital for medical image analysis and diagnosis. Brain tumors manifest in various forms, categorized as benign or malignant based on their tissue characteristics. Convolutional Neural Networks (CNNs), particularly VGG-16, Inception-v3, and ResNet-50 are prominent in brain tumor analysis, segmentation, and classification. These models are compared for their efficacy in identifying and forecasting brain tumor cells. Common brain tumor types include glioma, meningioma, and pituitary tumors, each requiring specific treatment approaches. © 2024 IEEE.

关键词： Tumors

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Adversarial Machine Learning Attack on Machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Massa, Joaquin Kim, Kyoung-Tak Kim, Taesic Ahn, BoHyun Venayagamoorthy, Kumar Zeng, Jianwu Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States Soongsil University Dept. of Electrical Engineering Seoul Korea Republic of University of Missour-Columbia Dept. of Electrical Engineering and Computer Science Columbia United States Clemson University Dept. of Electrical and Computer Engineering Clemson United States Minnesota State University Mankato Dept. of Electrical and Computer Engineering and Technology Mankato United States

ISBN: (纸本)9798350376067

Adversarial machine learning (ML) attacks are stealthy attacks designed to mislead the ML model results. This paper explores adversarial ML attacks that generate adversarial noisy input data in an ML-based controller in a solar inverter. Three types of ML models, long short-term memory (LSTM), gated recurrent unit (GRU)), and bidirectional-LSTM (Bi-LSTM), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial ML attacks (Basic Iterative Method (BIM) attack and Fast Sign-Gradient Method (FGSM)) are applied to the ML controllers. It is observed that the adversary ML attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the ML-based controllers. Moreover, the BIM attack is more effective than FGSM and Bi-LSTM-based controller is relatively robust to the attacks compared to peer. © 2024 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Cucumber Agriculture: AI for Precision Classification of Leaf Diseases 6

Revolutionizing Cucumber Agriculture: AI for Precision Class...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Shrestha, Tahmid Enam Aurnob, Al Rafi Tanim, Sharia Arfin Islam, Maruful Nur, Kamruddin Department of Computer Science Dhaka Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

Cucumbers are crucial agricultural commodities worldwide, necessitating production enhancements and quality maintenance. However, several diseases can easily hamper cucumber production if not classified and detected early. This study proposes a modified deep learning approach for identifying and classifying cucumber diseases at early stages using the Kaggle dataset. We applied data augmentation techniques to enhance the robustness of our model. We replaced the original MobileNetV2 classifier with custom classifiers, including linear layers, batch normalization, and ReLU activations to identify the effective features. Our proposed model achieved a high accuracy of 96.81%, which surpasses the accuracy of recent existing models in cucumber leaf disease classification. In the future, we plan to use a larger dataset, and this model can be adapted to work with various IoT devices for deployment in real-time applications. © 2024 IEEE.

关键词： Deep learning

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Analysis and Ranking Resume using Machine Learning Algorithms and NLP 1

Analysis and Ranking Resume using Machine Learning Algorithm...

引用

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

作者： Deshmukh, Pratik Chandrasen Bendre, M.R. Pravara Rural Engineering College Dept. of Data Science Engineering Loni India Pravara Rural Engineering College Dept. of Computer Engineering Loni India

ISBN: (纸本)9798350356816

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited. © 2024 IEEE.

关键词： Adversarial machine learning

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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Vehicle Detection and Speed Estimation Using Deep Learning 6

Vehicle Detection and Speed Estimation Using Deep Learning

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mishra, Vishnu Kumar Mishra, Megha Tiwari, Rajesh Kumar, T. Ram Diwan, Tarun Dhar Patra, Raj Kumar Malla Reddy Engineering college for Women Dept. of Computer Science and Engineering Hyderabad India Csvtu Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Bhilai India CMR Engineering College Department of Computer Science and Engineering Hyderabad India Atal Bihari Vajpayee University Dept. of Computer Science and CoE Chhattisgarh Bilaspur India

ISBN: (纸本)9798331505790

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation. © 2024 IEEE.

关键词： Convolutional neural networks

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