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Diagnosis of Skin Cancer Using Feature Engineering Techniques 3

Diagnosis of Skin Cancer Using Feature Engineering Technique...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Sannigrahi, Anindyadeep Singh, Vinayak Gourisaria, Mahendra Kumar Srivastava, Ritesh KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Galgotias College of Engineering and Technology Department of Computer Science Greater Noida India

ISBN: (纸本)9781665438117

Major mortality rate among human beings is due to cancer. Early diagnosis of Skin cancer especially Melanoma is having the potentiality to reduce morbidity as the major reason behind the disastrous repercussions of three out four homo-sapiens is due to skin cancer. Detection of cancer using machine learning and deep learning algorithms makes it very much feasible and economical. The ultimate focus of this paper is for detecting skin cancer at an early stage and helping to combat the increasing cases in skin cancer patients. In this paper, we have implemented different types of CNNs of different configurations on categorical classification where architectures were trained on different input image size and selecting of best architecture was based on various metric evaluations like Maximum Accuracy, Precision, Recall, and F1 score and best architecture has achieved high accuracy and performed outstandingly in all the evaluation section. Architecture 4 performed overall excellent in terms of every field of metric evaluations. This architecture will be a helpful tool for diagnosing skin cancer at an early stage and will take the less computational cost for classifying the skin cancer disease. © 2021 IEEE.

关键词： Convolutional neural networks

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Development of an Explainable AI (XAI) system for the interpretation of ViT in the diagnosis of medical images

Development of an Explainable AI (XAI) system for the interp...

引用

Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Soufiane Hachi Abdelouahed Sabri dept. of Computer Science Faculty of Sciences Dhar El Mahraz Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

ViT for vision Transformer have emerged as powerful deep learning architectures for medical image analysis, achieving performance in various diagnostic tasks. However, their complex internal mechanisms often hinder interpretability, limiting trust and hindering wider adoption in clinical applications. This study explores the development of an Explainable AI (XAI) system to interpret the decision-making process of a ViT model trained for skin lesion severity classification using the HAM10000 dataset. We leverage five explainability techniques —Attention Rollout, Beyond Attention, Generic Attention, Beyond Intuition, and Transition Attention Maps— to generate saliency maps, providing visual insights into the regions and features deemed most influential by the model for its predictions. Through saliency map visualizations, we demonstrate that the ViT model often learns to focus on clinically relevant features consistent with dermatological criteria, even without explicit feature annotations during training. However, the analyses also reveal potential biases, such as an over-reliance on color variations, and challenges in handling cases presenting with multiple diagnostic features. Our findings provide valuable insights for refining ViT models for skin lesion diagnosis and highlight the essential role of XAI in facilitating transparent and trustworthy AI-driven clinical applications.

关键词： computer vision Visualization Image analysis Explainable AI Refining Predictive models Transformers Skin Lesions Medical diagnostic imaging

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Decision support system of staff member promotion based on cloud computing (Case Study University Of Benghazi)

Decision support system of staff member promotion based on c...

引用

Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Entesar H. Alfsai Kenz A. Bozed Mohamed A. Hagal Department of Software Engineering University of Benghazi Benghazi Libya Department of Computer Science University of Benghazi Benghazi Libya

The scientific council at Benghazi University suffered from issues related to the scientific promotion process for faculty members. Promotion is still done manually, without a computerized system. Therefore, it faces obstacles in obtaining basic information, issuing promotion decisions, making managerial procedures, lack of flexibility, and the accumulation of papers. There are also unclear biases in decision-making and mechanisms of evaluating promotion criteria. Thus, they need to keep up with the international universities in the promotion process to be successful in administrative business today. Benghazi University needs an information system that can support decision-making, which is a major problem often occurs in the process of promotion of faculty *** use of decision support systems is a solution to reduce subjectivity in decision-making. This work presents an E-Gate that can assist in the promotion of educational staff. It is a system able to provide the best alternative in promotion educational staff. With the decision support system, the promotion process is carried out via the E-Gate using the AHP method according to several criteria. It is stored in the cloud computing because many enterprises are adopting the cloud-computing platform to examine the benefits of self-service, user guide, and high flexibility. The time is right for the educational universities to derive benefits of the cloud, as they are obliged to deliver better ubiquitous services. The study also developed Search Engine to verify the submitted research.

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Research on floating object ranging and positioning based on UAV binocular system 3

Research on floating object ranging and positioning based on...

引用

2021 3rd international conference on Artificial Intelligence and computer Science, AICS 2021

作者： Guo, Peng Zhang, Nengwen Jiang, Bing Qin, Fatao Zhou, Zhiheng School of Electrical and Information Engineering Wanjiang University of Technology Anhui Ma’anshan243011 China College of The IOT Engineering Hohai University Jiangsu Changzhou213022 China Jiangsu Xindaoge Automatic Control Solutions Co. Ltd. Jiangsu214432 China

Aiming at the ranging and positioning problem of floating objects polluted by water surface, the binocular system of UAV is designed to obtain the parallax and distance information of target objects in the scene. Firstly, Zhang Zhengyou calibration method is used to calibrate binocular camera;Secondly, stereo correction is carried out on the image to obtain a standard binocular stereo vision model;Finally, the depth information of the target object is obtained by stereo matching algorithm. The simulation results show that the depth error is less than 5%, and the actual physical coordinate position of the floating object is obtained by combining the GPS information of the UAV itself. © Journal of Physics: conference Series 2021.

关键词： Binocular vision

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An IoT based industry 4.0 architecture for integration of design and manufacturing systems 3

An IoT based industry 4.0 architecture for integration of de...

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3rd international conference on Materials, Manufacturing and Modelling (ICMMM)

作者： Anbalagan, Arivazhagan Moreno-Garcia, Carlos Francisco Robert Gordon Univ Sch Engn Garthdee HouseGarthdee Rd Aberdeen AB10 7QB Scotland Robert Gordon Univ Sch Comp Garthdee HouseGarthdee Rd Aberdeen AB10 7QB Scotland

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data through the Cloud/Web-based (CW) services and discusses an effective way to integrate them. In the 1st stage, a Radio-Frequency IDentification (RFID) technology containing computer Aided Design (CAD) data/models of the product with the ability to design / redesign is scanned and sent to a secure Internet/Cloud Server (CS). Here the CAD models are auto identified and displayed in the Graphical User Interface (GUI) developed for the purpose. From the scanned RFID CAD data/models, the 2nd stage adopts unique machine learning technique(s) and identifies the design & manufacturing features information required for product manufacture. Once identified, the 3rd stage handles the necessary modelling changes as required to manufacture the part by verifying the suitability of process-based product design through user input from the GUI. Then, it performs a computer Aided Process Planning (CAPP) sequence in a secure design cloud server designed using web-based scripting language. After this, the 4th stage generates computer Aided Manufacturing (CAM) toolpaths by continuous data retrieval of design and tooling database in the web server by updating the RFID technology with all the information. The various processes involved the 3rd and 4th stages are completed by using 'Agents' (a smart program) which uses various search and find algorithms with the ability to handle the changes to the process plan as required. Finally, the 5th stage, approves the product manufacture instructions by completing the production plan with the approved sheets sent to the computer Numerical control (CNC) machine. In this article, the proposed architecture is explained through the concept of IoT data transfer to help industries driving towards Industry 4.0 by im

关键词： Industry 4 0 Design and manufacturing systems (DMS) Internet of things (IoT) Cyber physical environment (CPE) Machine learning (ML) Agent based systems (ABS)

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VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

引用

Disruptive Technologies (ICDT), international conference on

作者： Payal Chhabra Tanmay Arora Vibhore Jain Neeraj Gandhi Swapnil Bhatnagar Nitin Arora Department of (CSE-AIML) KIET group of institutions Ghaziabad India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

There are approximately 21 million visually im-paired individuals who depend on other people for their basic work. This research adds to the current understanding of assistive technology by introducing a method for choosing and optimizing the best object detection models suited for accessi-bility environments. This research enhances the Visicompanion functions and assists visually impaired individuals in doing crucial daily tasks independently by selecting a suitable model. This research compares the analysis of two models i.e. Y oLov8 [1] and YOLOV9 [2] and identifies which algorithm is best for the application. YOLOv8 is known for its rapid inference speed which is beneficial for real-time systems whereas YOLOv9 introduces advanced detection capability that improves accuracy but may increase problems for processing time and computational resource requirements. This research uses evaluation metrics like precision, recall, size of model, and resource utilization across different hardware configurations. The results showed thatYOLOv9 is slightly better than YOLOv8 with mAP reaching 95% and precision 96.8%.

关键词： Measurement computer vision Computational modeling Object detection Assistive technologies Real-time systems Inference algorithms Hardware Resource management Disruptive technologies

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Design and Implementation of Intelligent Security Robot Based on Lidar and vision Fusion 3

Design and Implementation of Intelligent Security Robot Base...

引用

2021 3rd international conference on Robotics, Intelligent control and Artificial Intelligence, ICRICA 2021

作者： Zhou, Bo Du, Minglin Chen, Zhenguo Liu, Yajun Zhang, Yan Wang, Yan School of Information Beijing City University Beijing China Hebei IoT Monitoring Eng. Technology Research Center North China Institute of Science and Technology Hebei China

Nowadays, the mainstream security robot is expensive and the single 2D Lidar SLAM robot has large pose error and large matching calculation in navigation. To solve these problems, this paper designs an intelligent security robot based on the ROS system and STM32 microcontroller. Through the fusion of camera, IMU, lidar multi-sensor data, completing its own positioning, surrounding environment mapping and autonomous navigation functions;Binocular camera for face recognition, lock suspicious persons;Combined with temperature and humidity, smoke and other sensors for environmental monitoring, real-time alarm;Security personnel remote control, according to the data uploaded by the robot to complete the patrol. After a lot of experiments, the functions of the robot are better completed, which has a certain practical value. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Research on Multimodal Sentiment Analysis Technique Based on Global Attention Mechanism 3

Research on Multimodal Sentiment Analysis Technique Based on...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Wang, Shoujin Zhu, Xiaobin Jin, Ning School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798331508456

Emotion analysis, as the most active research field at present, has gradually moved from single mode to multi-mode. The purpose is to identify and understand human emotion state more accurately and analyze and understand complex emotional information more comprehensively by comprehensively utilizing the information in multiple modes (such as language, vision, audio, etc.). It has been developed to develop complex feature fusion techniques. At present, researchers are committed to the development of complex feature fusion techniques. However, due to the differences in the dimensions of different modes, the fusion process faces many challenges, which becomes the bottleneck of achieving efficient sentiment analysis. Aiming at this challenge, this paper proposes a solution of sentiment analysis based on global perception ability to effectively improve the fusion effect between modes and improve the accuracy of sentiment analysis. Based on the MISA framework, we optimized the mode processing method, projected each mode into two identical intervals of complementing each other. By introducing the strategies of invariant mode and specific mode, we solved the problem of commonness and characteristics in multi-mode fusion, reduced the gap between modes, and retained the unique characteristics of each mode. In doing so, the Global View Attention Mechanism (GVT) is combined to better capture long-distance dependencies and global features, enabling more accurate prediction of sentiment analysis tasks. Experimental results show that the optimized model performs significantly better than the existing baseline methods on MOSI and MOSEI public datasets. © 2025 IEEE.

关键词： Emotion Recognition

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Path Tracking control of Four Wheel Unmanned Ground Vehicle Using Optimized FOPID controller 3

Path Tracking Control of Four Wheel Unmanned Ground Vehicle ...

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3rd international conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Haruna, Zaharuddeen Mu'azu, Muhammed B. Abubakar, Yusuf S. Adedokun, Emmanuel A. Ahmadu Bello Univeristy Department of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781665438971

This research presents the path tracking control of a four-wheel Unmanned Ground Vehicles (UGV) using an optimized Fractional Order Proportional Integral Derivative (FOPID) controller. The kinematic model of the UGV is modelled and simulated using Simulink to track a path generated on the basis of the navigation behaviour of the bat. The trajectory tracking error recorded is minimized by developing an optimized FOPID controller using Elite Opposition Bat Algorithm (EOBA). This improved the tracking accuracy of the UGV in following the trajectory generated. The developed control system (FOPID-EOBA) is implemented in MATLAB R2020a, evaluated using tracking error, tracking accuracy, and settling time and compared against the PID-GA based system. The simulation results showed that the FOPID-EOBA based system had an average tracking error of 0.028m, average tracking accuracy of 96% and settling time of 0.75 seconds, while the PID-GA based system obtained an average tracking error 0.083m, average tracking accuracy of 88% and settling time of 15.03 seconds. This implies that the FOPID-EOBA based controller tracks the path generated faster with better tracking accuracy than the PID-GA based controller. © 2021 IEEE.

关键词： Three term control systems

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Sample Augmentation and Orientation-aware Deep Neural Network for Logistics Objects Detection 18

Sample Augmentation and Orientation-aware Deep Neural Networ...

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18th IEEE conference on Industrial Electronics and Applications, ICIEA 2023

作者： Yan, Wu Liu, Dong Zhai, Xueqian Zheng, Haochen Xu, Zhihao Pan, Jia Wu, Hongmin The Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou China The Pazhou Lab Guangzhou China The University of Hong Kong Hong Kong The Wuyi University Jiangmen China

ISBN: (纸本)9798350312201

Object detection is an important task in computer vision, with wide-ranging applications from autonomous driving to Logistics including image and video analysis. Among these, oriented object detection has emerged as a promising approach for detecting and localizing objects in images with arbitrary orientations. While traditional object detection methods rely on horizontal bounding boxes so that it is difficult for detecting oriented objects, oriented object detection leverages the rich representation power of deep neural networks to learn object-oriented features that capture both the appearance and orientation of objects in images. This paper introduces an orientation-aware neural network detector called Directional R-CNN with sample augmentation. The proposed framework addresses the issue of over-fitting by using various data augmentation techniques to improve the quality and quantity of the image samples. Furthermore, the effectiveness of the proposed approach is extensively evaluated through experiments conducted on real-world logistics applications, specifically for detecting oriented objects. The results indicate that the detection of enhanced image samples outperforms other methods with the mAP of 93.5%. © 2023 IEEE.

关键词： Object detection

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