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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Ahmmed, Tomal Tahsin Faruk, Md. Farukuzzarnan Srizon, Azmain Yakin Mahedy Hasan, S.M. Khansur, Md. Nasif Efat, Anwar Hossain Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are irregularities on the skin, often harmless, though some may progress to skin cancer. Detecting them early is crucial for successful treatment and prevention of cancer development. Traditional detection methods involve significant expenses and testing. This study introduces an auto-mated system for classifying skin lesions using a Shallow Tuned DenseNet (STDN), a compact Convolutional Neural Network (CNN) model. We employ Transfer Learning (TL) by fine-tuning a pretrained DenseNet-169 model with additional lightweight layers, allowing the STDN to adapt to the PH2 dataset's specific characteristics while remaining efficient. Initially, we utilize data augmentation techniques to address class imbalances within the dataset, improving model performance by reducing bias towards the majority class. The evaluation demonstrates a promising hundred percent accuracy with perfect F1-score and specificity. We attribute these results to effective transfer learning utilization, enhanced tuning, and partially to data augmentation, which mitigates overfitting and biases, facilitating robust feature extraction. Furthermore, we employ Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize and interpret the model's predictions, enhancing trust and comprehension. The proposed STDN model makes a significant contribution by achieving high accuracy with a lightweight design, offering a potentially cost-effective and efficient solution for automated skin lesion detection, thereby aiding in early cancer prevention. © 2024 IEEE.

关键词： Convolutional neural networks

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An Explainable Deep Learning Framework for Multi-Class Skin Lesion Classification While Resolving Class Imbalance 2

An Explainable Deep Learning Framework for Multi-Class Skin ...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Jahan, Israt Efat, Anwar Hossain Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Srizon, Azmain Yakin Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are indicative of various skin diseases, including serious conditions like melanoma, which can be life-threatening if not detected early. Automated systems for analyzing dermatoscopic images have shown promise in improving diagnostic accuracy. However, many existing studies rely on pre-trained convolutional neural network (CNN) architectures without customization, leading to potential biases and suboptimal performance. In this study, we propose a customized DenseNet121 architecture tailored for skin lesion classification. We address class imbalance issues through data augmentation and fine-tune the model to extract fine-grained features. Our method surpasses other cutting-edge models in terms of accuracy, precision, recall, specificity, sensitivity, and root mean square (ROC) values. Furthermore, we utilise Gradient-weighted Class Activation Mapping (Grad-CAM) to visually represent the decision-making process of the model. The next study will prioritise the validation of the model using a wide range of datasets and the integration of lesion-related information to enhance its performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodal and Multi-Temporal in Satellite Images Based on Flood Detection: A Comparative Study 2

Multimodal and Multi-Temporal in Satellite Images Based on F...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Sonavale, Amruta Chakkaravarthy, Midhun Rao, Surampudi Srinivasa Salleh, Hishamuddin Bin M. Jadhav, Jagannath Lincoln University College Department of Computer Science and Multimedia Malaysia Malla Reddy College of Engineering and Technology Department of Electronics and Communication Engineering Telangana India Kle College of Engineering and Technology Department of Electronics and Communication Engineering Chikodi India

ISBN: (纸本)9798350373110

Floods can cause significant damage to human lives and property, necessitating precise post-flood evaluations for rescue efforts. Methods: It explores flood location AI techniques using a multi-modular and multi-fleeting picture dataset, ranging from traditional methods like multi-facet perceptron (MLP), support vector machine (SVM) and Deep convolution neural network (DCNN) to space transformation-based strategies. We utilized SPOT-5 and RADAR pictures from the November 2000 flood in Gloucester, UK. Results: The F1 score of 0.8846 and the region under the accuracy (AUC) bend (PR) of 0.9173 in the examinations were marginally higher than those that used machine strategies and investment of human resources. Conclusion: Utilizing a multi-modular and multi-temporal image dataset looked at a few customary flood location AI techniques as MLP, SVM and DCNN, to later space transformation-based strategies. This enhancement is due to the 20 labelled data samples used in the domain adaptation- based on semi-supervised domain adaptation (SSDA). © 2024 IEEE.

关键词： Investments

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Securing Birth Certifications using Blockchain 3

Securing Birth Certifications using Blockchain

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Arora, Manan Agrawal, Khushi Ambawde, Dayanand Sardar Patel Institute of Technology Electronics and Telecommunication Engineering Department Mumbai India Sardar Patel Institute of Technology Computer Engineering Department Mumbai India

ISBN: (纸本)9798350384598

Blockchain has been recognized as a secure technology with which unique and tamper-proof certificates can be issued. Today the secure and immutable storage of vital records, such as birth certificates, is of paramount importance. Traditional systems for recording and managing such documents are complex, tedious, and inaccessible for many. At present, issuing a certificate involves three stakeholders;Parents, Hospitals, and Registrars. The Parents must visit the Registrar and report the birth after which the Registrar verifies the birth, which is a time-intensive process for the Registrar. To address these challenges, we present a web application that leverages blockchain technology to record and manage birth certificates. By utilizing a decentralized and distributed ledger, our application improves accessibility, transparency, and increased security. This paper outlines the design, implementation, and evaluation of our birth certificate recording system, highlighting its benefits for hospitals, registrars, and individuals. Furthermore, limitations and the future work of this technology are discussed. © 2024 IEEE.

关键词： Smart contract

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A Secured Online Voting system using Visual Cryptography and Validation 4

A Secured Online Voting system using Visual Cryptography and...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sable, Sayali Ghadage, Tanuja Shelurkar, Arpit Moon, Sunil K. SCTR's Pune Institute of Computer Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350349900

Phishing poses a substantial threat to online voting, demanding robust prevention methods. Visual Cryptography (VC) offers promise, enhancing security and preserving voting system confidentiality. The Voting System Using VC enables secure remote voting, merging two VC shares to create a unique password - one emailed to voters before the election, the other accessible during the election. This combats phishing, ensuring secure ballot casting. Amid rising phishing risks, innovative prevention techniques are explored, including image-based VC verification and biometric finger sensors. The paper emphasizes internet voting's security, privacy, and stakeholder involvement, concluding that integrating VC and advanced anti-phishing measures comprehensively secures online voting systems against evolving threats. Additionally, online voting facilitates convenient voting for individuals from diverse locations and aids disabled individuals in casting votes from any place. This emerging field underscores the importance of addressing major threats to ensure the integrity and accessibility of the electoral process. © 2024 IEEE.

关键词： Phishing

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Wireless Gesture-Driven Navigation for Autonomous Robots: An Innovative Human-Machine Interface Approach

Wireless Gesture-Driven Navigation for Autonomous Robots: An...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Zarina Begum, M. Gowthami, V. Ramya, M. Vijaya Shanthi, V. Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering Puducherry India Christ Institute of Technology Department of Computer Science Engineering Puducherry India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

As more and more features are added to the car's Human- Machine Interface (HMI), device management becomes increasingly difficult. Making the most of the many human senses is consequently a tactic to facilitate using in-car technologies. With the use of an Arduino Uno and this concept, a car robot with wireless navigation can be built. The robot chassis's direction is altered in accordance with the statement specified in the Arduino code, which is run when the position of the hand is monitored and the designed coordinates are taken into consideration as the parameter. in order for it to carry out the necessary actions, such as going ahead, backward, turning left, turning right, and stopping. © 2024 IEEE.

关键词： DC motors

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Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

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Audio Deepfake Detection using Deep Scattering Network 9

Audio Deepfake Detection using Deep Scattering Network

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Avaiya, Utsav Badlani, Naman Baadkar, Rajas Talele, Kiran Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Electronics and Telecommunication Engineering Mumbai India

ISBN: (纸本)9798350377972

The rise of audio Deepfakes has raised serious concerns about the authenticity of audio content, affecting media, politics, and cybersecurity by enabling misinformation, fraud, and identity theft. To address these challenges, we propose a novel approach to Deepfake detection using a Deep Scattering Network (DSN). Unlike traditional methods that rely on Convolutional Neural Networks (CNNs), which require massive amounts of training data, our approach leverages the Scattering Transform to capture multi-scale time-frequency features, enhancing the detection of subtle anomalies in manipulated audio while requiring significantly less data for training. Additionally, DSNs employ pre-defined multi-scale wavelet filters rather than data-driven linear filters, offering a more interpretable model while retaining a hierarchical structure. Evaluation of a publicly available dataset shows that the DSN outperforms the standard CNN-based models, particularly in scenarios with low-quality or heavily distorted audio. This research underscores the potential of scattering networks in bolstering the security and reliability of audio authentication systems. © 2024 IEEE.

关键词： Authentication

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A Review of Enhancement in Security of an IoT System Using BlockChain 3

A Review of Enhancement in Security of an IoT System Using B...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karankar, Nilima Seth, Anita Institute of Engineering and Technology DAVV Department of Computer Engg. Indore India Institute of Engineering and Technology DAVV Department of Electronics and Telecommunication Indore India

ISBN: (纸本)9798350396157

The Internet of Things is thriving rapidly in terms of quality and quantity, helping us to provide better services every day. With the wide-spread growth of the Internet of Things, it introduces new technical challenges, such as managing a vast number of devices. In almost every field, like smart homes, smart transactions, automobiles, and healthcare, IoT is being used very effectively. Even though so many architectures and frameworks are proposed to overcome these challenges, security and scalability are major challenges for an IoT system. Additionally, IoT technology has been merged with other advanced technologies, such as cloud computing, big data, and blockchain, to form a strong IoT system. Blockchain is an emerging technology that is based on distributed protocols. It has the potential to ensure the security, scalability, and reliability of an IoT system and can provide secure digital transactions. This paper attempts to identify the current contributions to IoT security using blockchain and highlights various research gaps in this area. The paper concludes with a discussion on open challenges and research issues. © 2024 IEEE.

关键词： Scalability

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Robust Multi-Dialect End-to-End ASR Model Jointly with Beam Search Threshold Pruning and LLM

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SN computer Science 2025年第4期6卷 1-12页

作者： Shunmuga Priya, M.C. Karthika Renuka, D. Ashok Kumar, L. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Information Technology PSG College of Technology Coimbatore India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

This paper aims to develop a novel robust multi-dialect end-to-end ASR system with beam search threshold pruning. The efficacy of our proposed model is evaluated using word error rate (WER). Our key contributions are: (1) To develop an end-to-end ASR system using attention-based neural network architecture and analyze the effectiveness of two features such as MFCC and log mel filter bank energies on multiple speech dialect corpora including American, Britain, and Indian accents;(2) To integrate beam search threshold pruning as a decoding mechanism to reduce the decoding time (3) To conduct an experimental analysis to test the model performance and compare the results against baseline system. (4) Post processing analysis are carried out using Llama2-7B based large language model(LLM) for enhancing the performance of proposed ASR system. The proposed model significantly improves performance by 1.91% and 4.29% over clean and noisy speech in librispeech corpus. Similarly, for the Indian accented speech, the model attains an average WER of about 6.6%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic speech recognition Beam search Decoding LLM Log Mel filter bank energies

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