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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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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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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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Simulating Application Driven Mobile D2D underlay Networks using Gym API 17

Simulating Application Driven Mobile D2D underlay Networks u...

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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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Bengali Intent Classification with Generative Adversarial BERT 26

Bengali Intent Classification with Generative Adversarial BE...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Hasan, Mehedi Ibna Basher, Mohammad Jahid Shawon, Md. Tanvir Rouf Ahsanullah University of Science and Technology Department of Computer Science and Engineering Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

Intent classification is a fundamental task in natural language understanding, aiming to categorize user queries or sentences into predefined classes to understand user intent. The most challenging aspect of this particular task lies in effectively incorporating all possible classes of intent into a dataset while ensuring adequate linguistic variation. Plenty of research has been conducted in the related domains in rich-resource languages like English. In this study, we introduce BNIntent30, a comprehensive Bengali intent classification dataset containing 30 intent classes. The dataset is excerpted and translated from the CLINIC150 dataset containing a diverse range of user intents categorized over 150 classes. Furthermore, we propose a novel approach for Bengali intent classification using Generative Adversarial BERT to evaluate the proposed dataset, which we call GAN-BnBERT. Our approach leverages the power of BERT-based contextual embeddings to capture salient linguistic features and contextual information from the text data, while the generative adversarial network (GAN) component complements the model's ability to learn diverse representations of existing intent classes through generative modeling. Our experimental results demonstrate that the GAN-BnBERT model achieves superior performance on the newly introduced BNIntent30 dataset, surpassing the existing Bi-LSTM and the stand-alone BERT-based classification model. © 2023 IEEE.

关键词： BERT Generative Adversarial Network Intent Classification

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

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

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

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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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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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Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Polycystic Ovary Syndrome (PCOS) diagnostic methods in machine learning: a systematic literature review

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Multimedia Tools and Applications 2025年第16期84卷 16301-16337页

作者： Arora, Shikha Vedpal Chauhan, Naresh J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Applications J.C. Bose University of Science and Technology YMCA Haryana Faridabad India Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Haryana Faridabad India

Polycystic Ovarian Syndrome, also known as PCOS, is a major hormonal imbalance affecting women primarily in their reproductive age. Women with PCOS may have either infrequent or extended menstrual cycles or sometimes excess male hormone i.e. androgen levels. The ovaries may grow with number of slight collections of fluid, called follicles that fail to release eggs every month regularly. It is also seen that PCOS not only affects a woman’s fertility but also indirectly causes various other health issues like type 2 Diabetes, obesity, blood pressure and other metabolic disorders. Recent researches have focussed on the use of different algorithms in Machine Learning to diagnose PCOS using structured or unstructured data. Therefore, in this study, a considerable Literature Review has been carried out to provide a detailed analysis of various algorithms that have been used to detect PCOS with their comparative study. Also some of the work particularly focuses on health issues, the food and dietary patterns and the ways to manage PCOS. Further, these algorithms are critically analysed to understand the framework and limitations that should be considered while putting forward the solutions relevant to the diagnosis of PCOS in an effective way. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood pressure

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