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Skin Lesion Classification using Deep Learning Architectures 2

Skin Lesion Classification using Deep Learning Architectures

引用

2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Gothivrekar, Siddhi Khanvilkar, Anuj Nandi, Utsab Singh, Kamal Maktum, Tabassum Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350372892

Skin cancer, originating from diverse dermatological conditions, can be classified based on texture, structure, and color. Identifying lesions from images aids in pre-diagnosis;expanding beyond melanoma, early detection of lesions like Actinic Keratosis and basal cell carcinoma is crucial for timely intervention. Recently, deep convolutional neural networks (DCNNs) have shown promising results in dermatological analysis. This paper presents a comparative study of various DCNNs for the categorization of skin lesions. The proposed method involves fine-tuning the popular architectures such as the VGG16, Inception V3, Inception ResNet V2, and DenseNet 201 trained using the HAM10000 dataset. The paper investigates fine-tuning just the top layers and all the layers of the models, aiming to identify the most effective approach. According to the work's experimental findings, fine-tuning the complete trained model performs significantly better. Moreover, the study finds that fewer epochs and less computational time are required for fine-tuning the entire model while achieving higher accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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DNA Sequence Alignment: Exploring All Potential Optimal Solutions 3

DNA Sequence Alignment: Exploring All Potential Optimal Solu...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ghosh, Rittija Ray, Sanchita Saha St. Thomas' College of Engineering & Technology Dept. of Information Technology Kolkata India

ISBN: (纸本)9798331523893

This paper presents a DNA Sequence Alignment approach that explores all potential optimal solutions for aligning pairwise DNA sequences. The suggested DNA sequence alignment technique introduces a new concept of the Direction matrix to explore all the possible DNA sequence alignments by traversing the specified directions within the matrix. The directions are symbolized by symbols "d"( for diagonal), "u"(for upwards), and "l"(for left). The presence of these symbols symbolizes the path that should be traversed within the Direction matrix to generate the paired sequence alignments. The proposed method has been fully experimented across six distinct cases, with sequence lengths ranging from 8 to 64 nucleotides, using 100 pairs of pseudo randomly generated sequences. Although, the proposed method takes longer alignment time as compared to the well-established Needleman & Wunsch algorithm, but is more efficient as it generates all possible optimal sequence alignments instead only one. © 2025 IEEE.

关键词： Direction Matrix DNA Sequence Alignment Globally Aligned Optimal DNA Sequence Alignments Score Matrix

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Flood Location, Management and Solution(FLMS): A Flood Prediction and Management System for Kurla 3

Flood Location, Management and Solution(FLMS): A Flood Predi...

引用

3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Naidu, Siddhanth Pandey, Ambuj Patel, Shiab Wagaskar, Kalpita Tripathy, Amiya Kumar Sen, Sumit Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India Indian Institute of Technology Bombay Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Flooding is a common problem in Kurla, a suburban area of Mumbai, India, with significant impacts on infrastructure, public health, and their livelihoods. This paper proposes FLMS, a Flood Location, Management, and Solution system designed to predict flood events in Kurla to assist in managing them. The FLMS system uses historical data on rainfall gathered from different sources, water levels, and other factors to develop a prediction model based on machine learning techniques. FLMS being a web based application will allow its users to view alerts, contact the respective personnel and create awareness about the different stages of a disaster and ways to limit the losses caused by floods. © 2023 IEEE.

关键词： Machine learning

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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy 9

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sami, Areesha Naseer, Anum Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan University Lahore Information Technology Pakistan

ISBN: (纸本)9798350394474

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally. © 2024 IEEE.

关键词： Support vector machines

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Novel Approach to Monitor Location of the Student in the Institution 15

Novel Approach to Monitor Location of the Student in the Ins...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kulkarni, R.N. Suma, Rochana Y. Sravanthi, B. Rakshitha, G. Krathika, T. Ballari Institute of Technology and Management Dept. of Computer Science & Engineering Ballari India

ISBN: (纸本)9798331300579

During the last few decades there is a tremendous growth in the information and communication technology wherein many organizations are making use of the hardware and software for various applications. Marking the attendance of a student in the school or college as well as the presence of the employee in the workplace is to be either captured or manually marked in the registers. Performing various analyses by taking the manual input is cumbersome because the number of students in the class or number of employees in the organizations are increasing. To overcome the difficulties of managing the things manually in this paper we are proposing an automated tool to capture the presence of the student in the specific location. This paper proposes a novel approach to mark the presence of a student in the college premises. The tool developed here is an Android Application (App) wherein the students can send the details to the App. Two modules are proposed here, one for the student and another for the Administrator. Through the student module, each student can login to the App and capture the self-image and send it to the Administrator Module. The captured image comprises the GPS details like Longitude, Latitude, Time, and date information. The information sent through the App determines the location of the student whether the student is in the college premise such as Class room, Laboratory and Library. The application which is managed by the teacher or the administrator or proctor will get the details about the student's image, GPS locations such as latitude, longitude, date and time. The mobile app verifies the GPS location and image of the student and compares it with the database. If the captured information matches with the appropriate GPS location then the students attendance is marked as present in that particular duration and appropriate files are updated. © Grenze Scientific Society, 2024.

关键词： Face recognition

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Optimal Deep Learning Models for Post-Flood House Detection in Drone Imagery

Optimal Deep Learning Models for Post-Flood House Detection ...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jain, Swati Sushmitha, M. Einstein, A Manish Mahapatra, Ansuman National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Floods remain a persistent threat to human lives and infrastructure. Identifying flood-impacted houses in drone imageries will support the rescue teams to prioritize rescue operations in highly affected areas. This study investigates the performance of various deep-learning detection models for post-flood house detection using drone imagery. Our research aims to identify the most suitable model and optimized parameters for accurately and efficiently identifying flood-affected houses, thereby facilitating timely disaster response and recovery efforts. The paper explores various state-of-the-art architectures and provides a thorough performance evaluation. © 2024 IEEE.

关键词： Flood damage

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Priority-Considered Channel Access for Registration-Backoff-Time (RBT) -based IEEE 802.11ah IOT Network 17

Priority-Considered Channel Access for Registration-Backoff-...

引用

IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Huang, Chung-Ming Chen, Chia-Hsiang Ma, Jianhua National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan Hosei University Faculty of Computer and Information Science Tokyo Japan

ISBN: (纸本)9798350351637

This work devises a High Priority Reservation - centric Registered Backoff Time -based (HPR-RBT) channel access method to enhance the performance of IEEE 802.11ah IoT network. The proposed HPR-RBT method allows the STAs that have high priority's uplinked data to reserve, which is through the claiming action, the privilege of channel access. HPR-RBT adopts the Trigger RAW mode of IEEE 802.11ah and the RBT mechanism. In HPR-RBT, the Restricted Access Window (RAW) of IEEE 802.11ah is divided into (1) a claiming RAW that contains multiple claiming slots and (2) a data RAW that contains multiple data slots. Each claiming slot is further divided into many tiny slots, each of which is for one STA to claim its channel access's reservation for its high priority's uplinked data. Each data slot is further divided into a scheduled sub-slot, in which the channel access of the high priority's uplinked data is scheduled, and an unscheduled sub-slot, in which the channel access of the regular priority's uplinked data is allowed. Furthermore, this work proposes a grouping mechanism to balance the load of high priority's uplinked data across RAW's data slots to prevent excessive high priority's uplinked data in any particular RAW's data slot. Through the grouping mechanism, the IEEE 802.11ah Access Point (AP) can assign the data slot that has the lowest load of high priority's uplinked data to a newly associated STA of the AP. Comparing with the legacy 802.11ah method, the experiment result shows that the proposed HPR-RBT method has (i) the shorter transmission latency and (ii) the better throughput when the number of STA becomes bigger. © 2024 IEEE.

关键词： Internet of things

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Water Region Extraction in Aerial Images Using Deep Learning

Water Region Extraction in Aerial Images Using Deep Learning

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Pandey, Nikita Kumar, Saumya Yadav, Satish Kumar Srivastava, Shashwat Singh, Shomil Gupta, Deepak Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350369175

Water bodies are essential component of Earth's surface and accurate water region mapping is crucial for disaster management and environmental monitoring. Existing methods to map water bodies relies on traditional modelling methods that need extensive manual calibration struggle with scalability and lack advanced pattern recognition. This paper evaluates the effectiveness of deep learning-based semantic segmentation models, DeepLabV3+ and U-Net, using aerial images resized to $\mathbf{2 5 6 x} 256$ pixels in mapping water regions in aerial images. DeepLabV3+, with a ResNet50 backbone, and U-Net are trained on a comprehensive flood-affected dataset with preprocessing techniques such as resizing and augmentation. Model performance is assessed using metrics like IoU, F1 score, accuracy, precision, and recall. The results indicate that DeepLabV3+ outperforms U-Net, showing promise for automating water region mapping and improving flood surveys and urban planning. © 2024 IEEE.

关键词： Semantic Segmentation

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