Skin cancer can be identified through dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), leveraging annotated skin images and Con...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Skin cancer can be identified through dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), leveraging annotated skin images and Convolutional Neural Networks (CNNs), enhances diagnostic accuracy. This paper presents an early skin cancer classification method using a soft voting ensemble of CNNs. Three benchmark datasets—HAM10000, ISIC 2016, and ISIC 2019—were used in this research. The process involved rebalancing, image augmentation, and filtering techniques, followed by a hybrid dual encoder for segmentation via transfer learning. Accurate segmentation focused classification models on clinically significant features, reducing background artifacts and improving accuracy. Classification was performed through an ensemble of MobileNetV2, VGG19, and InceptionV3, balancing accuracy and speed for real-world deployment. The method achieved lesion recognition accuracies of 96.32%, 90.86%, and 93.92% for the three datasets. The system’s performance was evaluated using established skin lesion detection metrics, yielding impressive results.
People with visual impairments frequently struggle to comprehend their environment. This study proposes a mobile application designed to provide real-time descriptions of activities occurring around visually impaired ...
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ISBN:
(数字)9798331529833
ISBN:
(纸本)9798331529840
People with visual impairments frequently struggle to comprehend their environment. This study proposes a mobile application designed to provide real-time descriptions of activities occurring around visually impaired individuals. By capturing live video through the mobile camera, the app generates a descriptive narrative of the video, which is conveyed to the user via audio output, enabling a more accessible experience of their environment. The system employs OpenCV for capturing visual data, while Tesseract OCR is used to identify and recognize text from the documents. The Google Text-to-Speech API converts this text into speech, using libraries like Pydub and Librosa for additional audio processing. The proposed system hence results in advancement of accessibility to surroundings, through automatic event localization and text-based descriptions of complex visual data.
The growing attack surface and interconnection of the Internet of Things (IoT) and Industrial IoT (IIoT) have presented major cybersecurity issues. This work presents a graph-based analytical approach for IoT and IIoT...
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ISBN:
(数字)9798331520182
ISBN:
(纸本)9798331520199
The growing attack surface and interconnection of the Internet of Things (IoT) and Industrial IoT (IIoT) have presented major cybersecurity issues. This work presents a graph-based analytical approach for IoT and IIoT environment network vulnerabilities assessment and attack pattern recognition. Synthetic graphs were created with the Edge-IIoTset dataset to simulate network traffic in both normal and attack conditions, encompassing Distributed Denial of Service (DDoS) and backdoor assaults. The framework utilizes centrality measurements and temporal traffic analysis to identify crucial nodes, access points, and compro-mised devices. This method offers practical insights for intrusion detection and threat mitigation, emphasizing the capability of graph-based techniques to improve 1oT/IIoT network security. The findings illustrate the scalability and feasibility of the framework for real-time security applications in industrial environments.
Data centers’ high energy consumption necessitates essential power-saving techniques for optimization. This paper proposes two effective strategies: server consolidation and precise air conditioning units. Server con...
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This paper focuses on enhancing the energy savings of EPON-based backhaul for 5G and beyond networks. Here, we introduce a novel technique of the desynchronization of wake-up cycles among Optical Network Units (ONUs) ...
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This paper provides an algorithm to detect COVID-19 from the chest X-Ray images using Two-class classification with DenseNet-121 and Support Vector Machine. For effective identification of patterns suggestive of COVID...
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A novel dual-core dual-mode class-F voltage-controlled oscillator (VCO) with wide frequency tuning range (TR) is proposed in this letter. It consists of two coupled parallel "8"-shaped inductor and two cross...
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Due to the worldwide COVID-19 pandemic, developing a specific drug to combat the Coronavirus Disease (COVID) has been essential. However, pinpointing potential adverse reactions to these drugs pose challenges in the q...
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The integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) is changing the face of healthcare by allowing AI powered predictive, data driven insights, enabling medical practitioners ...
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Genetic sequence identification from electrical characterization of single molecules has emerged as a promising alternative to traditional approaches. Since electrical data on single molecules is extremely noisy due t...
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Genetic sequence identification from electrical characterization of single molecules has emerged as a promising alternative to traditional approaches. Since electrical data on single molecules is extremely noisy due to the limitations of even state-of-the-art approaches, achieving high detection rates is challenging, particularly when the task involves being able to distinguish a sequence from its single base-pair mismatches. To address this issue, we propose an architecture based on combining a convolutional neural network with an ensemble learning method, XGBoost. In addition, four different input feature representations are considered, 1D conductance probability distributions and 2D conductance versus distance probability distributions which can be viewed as images, with or without averaging over the experimental parameters. The with averaging case corresponds to feature matrices derived from mixed datasets. We find that 2D probability distributions are helpful with respect to classifier accuracy, but averaged conductance probability distributions are much more impactful and significantly enhance prediction accuracy. Our quantitative analysis of multiple sequences shows an impressive performance increase of approximately 10% for all sequences. While the basis of our analysis is conductance data of DNA strands and their single base-pair mismatches, our method is generally applicable to other single-molecule identification based on their conductance.
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