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Education Certificate Storage and Verification Using Blockchain and Interplanetary File System 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Saiful, Md. Rana, Md. Sohel Shaeed, Nabila Ferdaus, Md. Hasanul Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Blockchain technology has gained popularity in recent years because of its ability to deliver safe and decentralized solutions for a wide range of sectors. A significant difficulty that educational institutions, businesses, and individuals all confront today is maintaining the security and legitimacy of academic diplomas. It can be difficult, time-consuming, and vulnerable to fraud to use conventional procedures to validate academic qualifications. The introduction of blockchain technology, however, presents a potentially viable answer to these problems. The solution suggests using a blockchain network to keep each academic degree as a digital asset. These digital certificates are time-stamped, cryptographically protected, linked to distinct identifiers like hashes or public keys, and are cryptographically secured to ensure their integrity and immutability. The MetaMask plugin and Ethereum network enable anybody with access to the blockchain network to validate the legitimacy of a certificate, doing away with the need for middlemen and cutting down on the likelihood of credential fraud. The paper’s key advantage is that the data that are kept in the blockchain are distinct identifiers of the encrypted material, which is encrypted using a mechanism that increases the security of academic credentials. Additionally, a lot of encrypted data is stored via the Interplanetary File System (IPFS). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Blockchain

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Multi-Class Brain Tumour Classification with CNN on MRI Scans

Multi-Class Brain Tumour Classification with CNN on MRI Scan...

引用

2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Mridha, Nisarga Rafa, Sofia Noor Shayon, Md. Siamul Islam Sultana, Nusrat Safin Kamal, K.M. Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9798331510213

The process of classifying brain tumors using MRI data is difficult to do accurately. The challenges and limitations of manual analysis in treatments can be overcome by implementing computer-Aided Diagnosis (CAD) systems for categorizing brain tumors. This study assesses the performance of five well-known Transfer Learning CNN architectures - VGG19, VGG16, GoogleNet, ResNet-50, and DenseNet-121 - using magnetic resonance imaging (MRI) scans. The main goal is to classify brain malignancies into four groups: no tumor, pituitary, meningioma, and glioma. The focus of this research for the classification task was on data preparation, training on preprocessed data, and comparing each model using evaluation metrics such as f1-score, accuracy, recall, and precision. Following pre-processing and training, the evaluation of the five CNN models revealed promising results for the VGG models, with both VGG19 and VGG16 achieving an accuracy score of 97%. This study indicates that among the base variants of Transfer Learning models, VGG19 and VGG16 are well-suited for classifying different types of brain tumors. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Contextual Aligned Tamil Sign Language Retrieval System with Semantic Masked Transformer

Contextual Aligned Tamil Sign Language Retrieval System with...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sivakavi, S Thillai Minu, R.I. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur603203 India

ISBN: (纸本)9798331521394

The sign language retrieval system, while similar to conventional video retrieval systems, faces distinct challenges that require a thorough understanding of the meanings conveyed through human actions depicted in the videos. Typically, meaningful information is extracted using RGB feature extraction techniques. However, this method can lead to redundancy, as it often fails to capture the global contextual information inherent in each video frame. Additionally, the reliance on RGB features can present significant memory challenges due to the large data requirements associated with processing and storing this information. Consequently, a more efficient approach is necessary to enhance both the accuracy and effectiveness of sign language retrieval. To address these issues, we have implemented a semantic masked transformer architecture specifically tailored for the sign language retrieval task. This innovative system not only incorporates RGB features but also integrates pose features, which are derived from the movements and positions of the signer. By fusing these two types of features based on their similarity, the system can provide a more refined and pertinent retrieval of textual information related to the video frames. Our extensive experiments conducted on a custom dataset designed for Tamil Sign Language demonstrate that this framework significantly outperforms current state-of-the-art methods. These results underscore the effectiveness of combining semantic understanding with advanced feature extraction techniques, ultimately leading to improved accuracy and efficiency in sign language retrieval. © 2025 IEEE.

关键词： Online searching

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NADR-Net: A Deep Learning Framework for Predicting Neurological Adverse Drug Reactions Using 17 Molecular Descriptors

NADR-Net: A Deep Learning Framework for Predicting Neurologi...

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2024 International Symposium on Computing and Intelligent Systems, SCI 2024

作者： Chaurasia, Anushka Kumar, Deepak Yogita Department of Computer Science and Engineering National Institute of Technology Meghalaya India Department of Computer and Engineering National Institute of Technology Kurukshetra India

Neurological adverse drug reactions (NADRs) pose a significant clinical challenge as they can have a profound impact on patient health and treatment outcomes. While diverse drug descriptors have been employed for neurological ADR prediction, the potential of using 17 molecular descriptors for this purpose has not been explored. To address this, a multilabel NADR-Net and MLSMOTE-based framework have been proposed for neurological adverse drug reaction prediction. The data for 17 MD and ADRs were extracted from PubChem and ADRECs databases and then mapped based on drug ID. The resulting dataset contained information on 2160 drugs, including their molecular properties, and 1030 ADRs. The methodology was then applied to this dataset, and it showed promising results in terms of hamming loss, precision, true positive rate, f1 score, and ROC-AUC. This study highlights the potential of using molecular descriptors for predicting neurological ADRs, which could improve patient outcomes and drug safety. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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SciTexC: A Framework for Scientific Text Classification in Bengali using Transformer-Based Techniques 4

SciTexC: A Framework for Scientific Text Classification in B...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Nahiyan, Fahmida Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798331530938

Scientific text classification (STC) in NLP automatically categorizes texts into predefined scien-tific domains. Although there is enormous progress in STC regarding high-resource languages, it must be explored in low-resource languages, specifically Ben-gali. This study introduces a framework, SciTexC (Scientific Text Classifier), which uses a transformer-based technique that classifies Bengali texts into six scientific domains: Physics, Chemistry, Biology, Information and Communication technology (ICT), Mathematics, and Others. A new corpus, STexD (scientific text dataset), containing 6000 Bengali texts, was also developed for classification tasks. Five transformer-based models were used to perform the task, including mBERT, dmBERT, Bangla BERT, Indic BERT, and XLM-RoBERTa. The evaluation results demonstrate that XLM-RoBERTa outperformed the other exploited models, achieving the highest F1 score (0.9158). © 2025 IEEE.

关键词： Classification (of information)

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Driving Efficiency: A Literature Review of Energy-Efficient Data Collection Strategies for Mobile Sink-Based Wireless Sensor Network 1

Driving Efficiency: A Literature Review of Energy-Efficient ...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Upadhyay, Priyanka Meena, Uma Rana, Kamlesh SRM Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Modinagar India Bharat Institute of Technology and Science Department of Computer Science and Engineering Uttar Pradesh Meerut India

ISBN: (纸本)9798350353648

Portable sinks assume a vital part in information assortment within wireless sensor networks (WSNs), offering dynamic and flexible solutions to address the limitations of static sink-based architectures. Unlike their static counterparts, mobile sinks have the capability to traverse the network, adapt their trajectories, furthermore, gather information straightforwardly from sensor hubs. This versatility empowers a few critical benefits in information assortment proficiency, energy streamlining, and network execution upgrade. This survey paper presents an extensive outline of the writing on energy-effective information assortment plans for portable sink- based remote sensor organizations (WSNs). With the expansion of Web of Things (IoT) applications, WSNs have become integral for collecting data from the environment in various domains. However, traditional static sink-based approaches suffer from energy imbalances and limited network lifespans. The integration of mobile sinks offers a promising solution to these challenges by enabling dynamic data collection and energy-efficient routing. © 2024 IEEE.

关键词： Data collection

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Energy-Efficient Encounter, Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks 2nd

Energy-Efficient Encounter, Buffer and Contact Duration-Bas...

引用

2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dutta, Amit Borah, Satya Jyoti Singh, Jagdeep Department of Computer Science and Engineering NERIST Nirjuli Itanagar India Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India

ISBN: (纸本)9789819736003

Opportunistic network (OppNet) is a type of advanced wireless mobile network that has evolved from Mobile Ad-hoc Networks (MANETs) and belongs to the category of Delay Tolerant Network (DTN). The unique features that make OppNet different from other wireless networks are its intermittent connectivity, unstable topology, no dedicated path to the destination from the source, and high node mobility. This further makes the process of data transmission a challenging task. For this a method called "store-carry-forward" is used in which the battery-operated intermediate nodes store the message, discover a suitable relay node, and forward the message to that node. This further leads to the depletion of the battery power or energy of the node in due course of time. In this context, there is a necessity to develop routing models by taking the current energy status of a node into consideration. In this paper, the authors proposed an energy-efficient version of a popular routing mechanism EBC, termed E-EBC where the selection of the next suitable node depends upon the battery power (referred to as Energy) of that node. This model is then compared with existing benchmark protocols- Epidemic, Prophet, EDR, E-Prophet, E-EDR, and EBC. Simulation results (using ONE Simulator) depict that the proposed model performs much better than that of the benchmark models respectively by around 42.73, 41.46, 58.19, 32.03, 65.99, and 44.77% in terms of average latency. The proposed protocol E-EBC is also compared against messages dropped by varying the TTL, Buffer Size, and number of nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Delay tolerant networks

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Simulating Application Driven Mobile D2D underlay Networks using Gym API 17

Simulating Application Driven Mobile D2D underlay Networks u...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Batabyal, Suvadip National Institute of Technology Department of Computer Science & Engineering Durgapur India

ISBN: (纸本)9798331531195

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short and low-power links between the devices. For this, the D2D paradigm is considered to be an integral part of all kinds of future networks and is an active area of research at different levels. This requires a simple, configurable, scalable, and realistic evaluation of the protocols proposed for the D2D networking paradigm. Although there exists a few platform to evaluate a D2D underlay network, they have one or more of the following drawbacks: (i) long development time and complex environment, (ii) limited support for mobility, (iii) no support for application specific development and evaluation. In this work, we developed a framework for simulating an application-driven mobile D2D underlay network using the Gym API, where D2D connections are made spontaneously based on the application requirements. The proposed framework includes modules for generating application-specific traffic, buffer for transmission and caching of application data, and mobility management. This complements several earlier works on application (or utility) driven resource allocation in D2D underlay network. The framework is used to evaluate a D2D network using different mobility models and performance of cache based video streaming in a D2D underlay network. Finally, the framework is compared with ns-3 and OMNET++ simulators and is observed to have lower CPU utilization and run-time under the same network settings. © 2025 IEEE.

关键词： Resource allocation

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Verma, Hritik Sarkar, Sayan Sadhukhan, Souranil Mishra, Aniket Mondal, Md Ashifuddin Narula Institute Of Technology Department Of Computer Science And Engineering India

ISBN: (纸本)9798350381665

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy. © 2024 IEEE.

关键词： Traffic congestion

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Deep Learning Models for Classification of Red Blood Cells in Microscopy Images for Anemia Diagnosis

Deep Learning Models for Classification of Red Blood Cells i...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Zaman, Navid Billah, Md. Farhad Haque, Kazi Ehsanul Morshed, Md. Adnan Safin Kamal, K.M. Morshed, Md. Ahnaf Reza, Ahmed Wasif Shamsul Arefin, Mohammad East West University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Anemia, a condition affecting human red blood cells (RBCs), presents in various forms. Different blood cell types and anemia variants exist. Addressing such a significant challenge requires integrating pathophysiology, advanced technology, and a comprehensive understanding of RBC classifications. In this pursuit, we utilize Deep Learning (DL) models to establish connections and propose innovative solutions to pathophysiological issues related to anaemia diagnosis through RBC classification. The customized Convolutional Neural Network (CNN) demonstrates exceptional performance, boasting a Training Accuracy of 99.42% and a Test Accuracy of 98.88%. Furthermore, the Training Loss is impressively low at 0.0232, while the Validation Loss remains minimal at 0.0964. The associated confusion matrix attests to the model's robust performance, affirming its accuracy in classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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