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共 26752 条记录，以下是151-160 订阅

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Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 2025 IEEE.

关键词： Convolutional neural networks

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Novel Genotypic Diversity Metrics for Real-Coded Optimization on Multi-Modal Problems 13

Novel Genotypic Diversity Metrics for Real-Coded Optimizatio...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mascarenhas, Alexandre Kobayashi, Yuta Aranha, Claus University of Tsukuba Dept. of Computer Science Tsukuba Japan

ISBN: (纸本)9798350308365

This paper presents two new Genotypic Diversity Metrics (GDMs) for estimating the diversity of populations in real-coded, multi-modal optimization problems. The two techniques introduced are the Minimum Individual Distance (DMID) and Radius Diversity (DRaD). The design of these two metrics focuses on the relative position of the solutions in the population. This choice addresses problems observed in traditional diversity measures regarding their behavior as the number of optima increases in a multi-modal problem. The proposed metrics are evaluated on four different experiments, which investigate the metrics' theoretical and practical properties. Experimental results reveal several abnormal behaviors of the Pairwise Distance Metric (Dpw), while showing that the proposed metrics perform in a more stable and intuitive way as the number of optima in the problem increases. These results recommend the metrics' use for developing and evaluating optimization meta-heuristics © 2024 IEEE.

关键词： Population statistics

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Wearable Sensor-Based Activity Recognition over Statistical Features Selection and MLP Approach 3

Wearable Sensor-Based Activity Recognition over Statistical ...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Nazar, Fakhra Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Supervising and examining the physical activities of different individuals by using wireless sensors is beneficial in favor of recognizing postural positioning and moving actions in the everyday ambiance. Accelerometers have been exercised in intelligent environments to identify human activities for many years. Using these accelerometer sensors and human activity recognition systems, people going through many sicknesses and illnesses can be proficiently scrutinized, and thus remedial medication can be dispensed in a well-timed approach. In this research paper, a naïve, and intricate technique founded on certain time domain features on the way to classify the physical activities is suggested. Feature extraction is a momentous step in HAR, and in this study, six statistical features were obtained from the preprocessed data. To further optimize the feature set, the Binary Grey Wolf Optimizer (GWO) was integrated. Further, a Multilayer Perceptron (MLP) was utilized for activity recognition to distinguish between the six activity classes. MLP has the advantage of finding non-linear patterns in data, the algorithm possesses powerful features of reducing overfitting in the data and good generality. It also offers resistance to noises usually experienced in the sensor data. Experimental results evaluated that the proposed framework achieved 87.14% recognition accuracy. This validates that the suggested framework can be used to observe physical activities and discriminate between healthy and unhealthy patients. ©2024 IEEE.

关键词： Wearable sensors

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Predicting Blood Hemoglobin Level With Machine Learning And Spectroscopy 2

Predicting Blood Hemoglobin Level With Machine Learning And ...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Kumar, Atul Kumar, Vikas Debnath, Drubajyoti Sorout, Deepansh Chawla, Pronika Tyagi, Shobha Dept. of Computer Science SET MRIIRS Faridabad India

ISBN: (纸本)9798331541217

Anemia, a widespread health condition characterized by low hemoglobin or red blood cell levels, remains a significant global concern, especially in resource-limited settings. The current methods for diagnosing anemia often require invasive blood tests, which can be costly and inaccessible for routine monitoring. This study presents a Python-based model aimed at providing a non-invasive, automated method for anemia detection by analyzing the conjunctival region of the eye. Utilizing Haar cascade classifiers for precise eye region detection and OpenCV for image processing, the model efficiently extracts and analyzes the RGB values of the conjunctiva. These values are compared to established thresholds indicative of hemoglobin deficiency, allowing for a preliminary diagnosis. The model, trained and tested with real-world image data, offers a promising solution to early anemia detection, combining rapid processing, cost-effectiveness, and accessibility. The strength of this work lies in its automation, affordability, and potential for application in remote or underserved areas where traditional diagnostic methods are not readily available. © 2024 IEEE.

关键词： Photoplethysmography

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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Remote Sensing based Human Monitoring and Recognition over Public Surveillance 3

Remote Sensing based Human Monitoring and Recognition over P...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Zahoor, Laiba Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Due to the ability to capture data from such distinct aerial perspectives, UAVs have gained numerous applications in drone imaging, human action recognition (HAR), search and rescue (SAR), and surveillance. However, such applications have consequences such as complex backgrounds and partial occlusions. This research presents a unique technique to tackle these issues for HAR using drone video data. We present an approach beginning with frame extraction from drone footage and preprocessing. First, we perform gamma correction and then we conduct background subtraction. YOLO detects humans, and key point extraction is achieved by extracting the important human joint points. The features were extracted, including full body features acquired by SIFT and LBP, key point features via spin images, and the Unique Shape Context (USC). Next, we improve these features using linear discriminant analysis (LDA), followed by a neuro-fuzzy classifier. The accuracy was assessed on the UAV Human and Drone Action datasets with scores of 56.4% and 86.7%, respectively. ©2024 IEEE.

关键词： Drones

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Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation 5

Efficient Aerial Images Algorithms over Multi-objects labeli...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Naseer, Aysha Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Aerial imagery is gaining significance across various domains, comprising agriculture, disaster management, and surveillance. Durin this study, a unique method to tackle the persistent problems in accurate and efficient multi-object recognition in aerial imagery has been introduced. Our suggested approach combines the abstraction of spectral and spatial texture details with the incorporation of a color space segmentation algorithm. We convert the original data into a single feature space by utilizing multiple kernel learning (MKL), which improves feature representation accuracy and, in turn, improves classification accuracy. Finally, various objects in aerial images may be recognized and categorized according to the application of markov random field. By addressing variances in illumination, orientation, and size, this novel approach seeks to provide a complete solution for improving multi-object recognition in aerial data. Our findings demonstrate a significant improvement in object detection accuracy 98.13%. © 2024 IEEE.

关键词： Object recognition

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U-NET based CT Image reconstruction and segmentation using Deep Learning Layer structure

U-NET based CT Image reconstruction and segmentation using D...

引用

2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Abbasi, Altamash Ahmad Farooqi, Ashfaq Hussain Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

CT imaging is critical for medical diagnostics, yet traditional reconstruction methods often struggle with noise and artifact reduction while maintaining image clarity. In this research work, an advanced methodology for computed tomography image reconstruction leveraging a U-Net model, combined with preprocessing through bilateral filtering and subsequent image segmentation. U-Net model is trained to reconstruct high-fidelity CT images from sparse-view data, ensuring superior detail preservation and artifact minimization. Our approach also incorporates a bilateral filter during preprocessing to enhance edge preservation and reduce noise. Post-reconstruction, we employ segmentation to isolate and highlight critical anatomical structures, further improving the interpretability and diagnostic value of the images. The de-noised image is then segmented using thresholding scheme. 2016 AAPM dataset has been used to check the effectiveness of the proposed methodology, demonstrating significant improvements in image quality metrics compared to conventional reconstruction techniques. Our results indicate that the integration of bilateral filtering and U-Net-based reconstruction, followed by precise segmentation, offers a robust solution for enhancing CT imaging quality. Our proposed approach has obtained PSNR of 30.461, NMSE of 0.036 and SSIM of 0.863. This approach holds substantial potential for clinical applications, providing clearer, more accurate images for diagnosis and treatment planning. © 2024 IEEE.

关键词： Image reconstruction

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Robust Machine Learning Approach to Plant Species Classification for Sustainable Agriculture 3

Robust Machine Learning Approach to Plant Species Classifica...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Sustaining resilient agricultural practices is essential for the overall yield because it guarantees both economic stability and food supplies. By incorporating Zero-Shot learning into a strong leaf classification architecture, our method meets this demand. We optimized an Al-powered four-step categorization process using the Bee Colony Algorithm. To pre-process, segment, extract features from, and classify leaf image data, zero-shot learning is employed. Pre-processing, which includes histogram equalization and contrast stretching, is followed by morphological segmentation. In feature extraction, two methods are used: Harris corner detection and SURF (Speeded Up Robust Features). Zero-Shot learning for classification is optimized using the Bee Colony Algorithm. At 92.73% accuracy, the suggested method surpasses out previous methods like CNN and SVM, suggesting that it has the potential to enhance crop quality and disease control in agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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A Robust Fraud Detection System using Deep Belief Network and Restricted Boltzmann Machines 7

A Robust Fraud Detection System using Deep Belief Network an...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Javeed, Madiha Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350368673

Online security has become popular as it delivers enriched monitoring services for digital world without obstructing the usage of online services. Several techniques have been introduced to increase the quality of security systems for online faulty transactions detection. An effective model has been proposed in this research by using various domain data learning processes, deep belief networks, and restricted Boltzmann machines to provide improved transactions monitoring. Two datasets have been utilized in proposed methodology to ensure that online systems are safe and secure. The datasets used are credit card fraud detection and abstract data set for credit card fraud detection. Deep belief networks and restricted Boltzmann machines combination has helped us improve the results of proposed system. An innovative prospect to researchers has been provided by proposing a security mechanism for credit cards-based fraud detection. It has been proved that the deep belief network has been able to outperform by achieving 99.2% mean accuracy. © 2024 IEEE.

关键词： Economic and social effects

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