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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Course construction based on FD-QM standards and analysis and mining of course data 24

Course construction based on FD-QM standards and analysis an...

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2024 International Conference on Intelligent Education and computer Technology, IECT 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400709920

In response to the shortcomings of the SPOC course "Introduction to Computational Thinking"at Beijing University of Civil engineering and Architecture, the teaching team has transformed and improved the course design and practice based on the existing High quality undergraduate courses in Beijing universities and Beijing Curriculum Ideological and Political Demonstration Course. We aim to maintain consistency and linkage in learning objectives, learning assessments, teaching materials, learning activities, and course technology under the principle of consistent construction. Learning objectives are clearer and more measurable;learning assessment is more scientific, diverse, and reliable;Curriculum resources can meet the needs of students at different levels and majors;learning activities can fully mobilize students' subjective initiative;Curriculum technology helps to achieve learning objectives, and can reflect the characteristics of the discipline, with universality and progressiveness. Through the construction and practice of the FD-QM standard, the course team has established the principles of course standardization, clarified the direction of course material construction, drawn a knowledge graph of course knowledge points, accurately grasped the degree of learning objectives, upgraded the "characteristic"teaching resource library, enhanced student interest, and integrated ideological and political education into the curriculum, ultimately achieving a comprehensive improvement in the quality of course teaching. © 2024 ACM.

关键词： Knowledge graph

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Lane detection networks based on deep neural networks and temporal information

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Alexandria engineering Journal 2024年 98卷 10-18页

作者： Lin, Huei-Yung Chang, Chun-Ke Tran, Van Luan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621 Taiwan School of Engineering Eastern International University Binh Duong Viet Nam

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. © 2024 The Author(s)

关键词： Network architecture

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Unveiling Security Vulnerabilities in NFTs: A Comprehensive Risk Assessment 4

Unveiling Security Vulnerabilities in NFTs: A Comprehensive ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Verma, Ravi Chandrawanshi, Kamlesh Soni, Gaurav Jain, Garima Nigam, Shekhar Jain, Neelesh Vit Bhopal University School of Computing Science and Engineering Bhopal India Samrat Ashok Technological Institute Department of Computer Science Vidisha India Sam Global University School of Computer Science and Engineering Bhopal India Prestiage Institute of Science and Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331518981

The biggest invention of Non-Fungible Tokens System (NFTs), that defines its role digitally using the self-generated concept of proof of ownership, which comes as a result of continues progress and advancement in Blockchain Technology This study aims to provide a comprehensive understanding of NFTs, their foundational technology, and the platforms enabling their trade. It highlights the key challenges of implementing NFT technology, focusing on issues related to ownership, governance, security, privacy, and environmental concerns. The research explores the inherent risks in the evolving NFT ecosystem, offering insights into the nature and scale of these threats. Additionally, it proposes a set of actionable solutions for participants and stakeholders to mitigate these risks. By categorizing core hazards within a simplified taxonomy, this paper identifies key vulnerabilities and outlines strategies for addressing them to enhance the safety and sustainability of the NFT space. © 2024 IEEE.

关键词： Differential privacy

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Optimized Biometric Key Management System for Enhanced Security 11th

Optimized Biometric Key Management System for Enhanced Secu...

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11th International Conference on Intelligent computing and Applications, ICRTC 2023

作者： Kaur, Prabhjot Kumar, Nitin School of Computing DIT University Uttarakhand India Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering PEC Deemed to be University Chandigarh India

ISBN: (纸本)9789819717231

Biometric cryptosystems are security tools that use an individual’s distinctive physical or behavioral characteristics to secure sensitive data. Any biometric cryptosystem’s effectiveness is dependent on how well the features are extracted. The suggested solution uses an evolutionary algorithm to first optimize the retrieved attributes. Second, fuzzy vault is used to safeguard these optimized features. The fuzzy vault system is a well-known biometric cryptography device that creates a vault by using a polynomial equation, protecting the biometric information. However, if an attacker has access to the polynomial equation, the security of the fuzzy vault scheme may be jeopardized. The shuffled frog leaping algorithm (SFLA) method has been suggested as a way to improve the security of the fuzzy vault technique in order to overcome this drawback. The SFLA is a meta-heuristic optimization method that resolves challenging optimization issues by simulating the social behavior of frogs. This meta-heuristic technique is used in this paper’s suggested strategy to produce and secure optimized points. The experiments are performed onn AMI and MMU2 databases. The results achieve higher levels of performance compared to the traditional schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optimization

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Leveraging EfficientNetB4 Model with Multi-head Attentions for Maize Leaf Disease Detection 2nd

Leveraging EfficientNetB4 Model with Multi-head Attentions ...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Reddiar, Nallamilli Eswar Venkata Vikranth, Pilla Veera Satya Sai Kumar, Teerdhala Siddartha, Thota Rayvanth, N. Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031845420

Smart agriculture mainly stresses the improvements in early plant disease diagnostics, crop classification and management, and effective pest control. Maize being an important staple crop necessitates early and accurate disease detection on its leaves. Hence, this paper proposes a novel Convolutional neural network model based on EfficientNet-B4 model and multi-head attention for the effective classification of maize leaf diseases. The proposed model focuses on early-stage disease patterns with the help of a large amount of data. Compared to other architectures, it attains an outstanding F1-score of 96.75%. The proposed system not only helps farmers to get timely diagnosis but also offers them useful information to mitigate risks and improve production. This study benefits agricultural resilience, food security and farmer’s welfare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Insect control

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Deep Learning for Enhanced Detection and Characterization of Pulmonary Nodules 4

Deep Learning for Enhanced Detection and Characterization of...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Lavanya, G. Muthulakshmi, M. Latha, Manohar Keerthinathan, A. Vishal Krishh, P. Sulthana, Sheba Meghana, Meka Kavya Uma Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350349900

This study aims to develop, an effective customized Convolutional Neural Network model that can accurately identify areas on CT scans that have lung nodules and those that do not. The study utilizes two datasets, the LUNA-16 dataset for training and the IQ-OTH/NCCD dataset for testing. Additionally, we employ a U-Net model for precise lung nodule segmentation. This segmentation approach aimed to achieve a comprehensive characterization of nodules based on their spatial characteristics. The testing phase involves evaluating the performance of our trained models on the IQ-OTH/NCCD dataset, particularly focusing on localizing nodules in malignant cases. Overall, the research highlights the effectiveness of deep learning in early detection and diagnosis of lung cancer. This work contributes to improving the accuracy and reliability of automated pulmonary nodule detection systems, with a focus on identifying nodules in patients with suspected malignancies. © 2024 IEEE.

关键词： Lung cancer

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Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study 9

Machine Learning-Based Detection of Blood Cancer from Micros...

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

作者： Manoj, Milan Sreekumar, Vishnu Reghuram, Surya Anjali, T. Ramlal, Nandakishor Prabhu Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Altair Engineering Ab Stockholm Sweden

ISBN: (纸本)9798350377972

Early and accurate detection of blood cancer is critical for improving patient outcomes. This article is proposed to develop and evaluate the effectiveness of using machine learning techniques for automation in blood cancer detection based on microscopic images of blood cells. A comparison will be conducted using three of the most used models: Support Vector Machine, Decision Tree, and Random Forest on their capability in the classification process of distinguishing blood cells from benign and malig-nant groups. The methodology is divided into the following steps: image preprocessing, extraction of color histogram and texture features through the Gray-Level Co-occurrence Matrix (GLCM), and optimization of hyperparameters for model training. Evaluation metrics used are accuracy, precision, recall, and F1-score. Random Forest has been seen to perform with a high accuracy of 99.08% with all the categories showing robust classification. It was followed by SVM with an accuracy of 96.15 % and Decision Tree with 94.92%. The results present Random Forest as a valuable resource for the automated diagnosis of blood cancer. This paper reiterates the promise that machine learning holds in boosting diagnostic efficiency and encourages future studies into more advanced approaches such as deep learning for further improvement in clinical applications. © 2024 IEEE.

关键词： Diseases

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Smart Gas Monitoring System for Classroom Safety 5th

Smart Gas Monitoring System for Classroom Safety

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Chandra Karuturi, Prudhvi Sai Hothur, Sai Chandra Pavan Goli, Sai Sri Kadiyala, Karthik Sri Sai Ullas, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819788644

In the current educational landscape, ensuring the safety of students and educators is crucial. Our research introduces a smart gas monitoring system designed specifically for student safety. In today’s classrooms, potential gas leaks pose a safety risk. Our solution involves strategically placing sensors within classrooms to detect harmful gases. These sensors continuously monitor air quality and, in the presence of hazardous conditions, send instant alerts to users through a user-friendly Android app. The Android app provides real-time gas concentration levels, allowing users to stay informed about their environment. By combining accessible technology with a user-friendly interface, our Smart Gas Monitoring System offers a practical and scalable solution to enhance safety in classrooms. This research aligns with the growing need for modern, reliable safety measures in educational settings, making it a valuable addition to existing safety protocols. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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