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Enhancing the Channel Quality in Cognitive network by Traffic Reduction Method 3

Enhancing the Channel Quality in Cognitive network by Traffi...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Singh, Sanjay Pilania, Urmila Kumar, Manoj Awasthi, Satya Prakash Nelson, Leema Manav Rachna University Dept. of Computer Science & Technology Faridabad India Poornima University Faculty of Computer Engineering Jaipur India Chitkara University Institute of Engineering & Technology Chitkara University Department of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350338607

Cognitive radio (CR) has some specific unique characteristics like flexibility, interoperability and adaptability. Optimal technological candidate is particularly contributed which reduces the challenges towards spectrum scarceness sin multimedia communications. Moreover, this communication over CR networks produces few severe issues like delay-sensitive, bandwidth-hungry, and loss-tolerant service and particularly ensuring quality of service (QoS). Improved Dynamic Optimization Algorithm (IDOA) is proposed for spectrum sensing in cognitive radio networks (CRN) where for the Secondary User (SU), data traffic is considered. From the comparison of several works in the literature, it is assumed that SU have no packets to transmit always;but actually data sent per second is considered instead of channel capacity as the possible throughput with which the problem of sensing-throughput tradeoff is reformulated. The parameters such as probability of availability, Signal to Interference Noise Ratio (SINR), Processing time and transmission time are considered to analyze the efficiency of the IDOA approach proposed which is compared with two state of methods namely Medium Access Control (MAC) and Dijikstra Algorithm (DA). © 2023 IEEE.

关键词： Cognitive radio

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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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Towards Better Misogyny Detection in Bangla: Improved Dataset and Cutting-Edge Model Evaluation 6

Towards Better Misogyny Detection in Bangla: Improved Datase...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Roy, Debopriya Deb Noumi, Israt Moyeen Rahman, Md. Aminur Ahsanullah University of Science and Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

Misogyny is hatred, disdain, or prejudice toward women or girls. It can be verbal abuse, aggression, harassment, or discrimination. In Bengali NLP, the scarcity of well-balanced datasets for misogyny detection is a significant problem. While there has been previous work in this area for the Bangla language, the existing dataset, which only contains 2258 comments, has proven insufficient for robust detection, resulting in suboptimal performance in classification models. To tackle this, we have carefully curated a dataset from different sources, including social media and different websites. This dataset features five categories of misogynistic comments (Discredit, Stereotype & Objectification, Sexual Harassment, Threats of violence, Dominance & Derailing) and one category of Non-misogynistic comments. We evaluated the performance of multiple Machine Learning models such as SVM(Support Vector Machine), Naive Bayes, KNN, Decision Tree, and Random Forest. Bangla BERT base and Bangla BERT Electra, two Deep Learning models, were also used for classification. Additionally, we employed ensemble approaches using Bangla BERT embeddings (Bangla BERT Base and BERT-base-multilingual-cased) with models like LSTM, Bi-LSTM, and CNN+LSTM. Among them, Bi-LSTM with BERT (Bert-base-multilingual-cased) Embeddings achieves the highest accuracy, 91.24%, in classifying misogynistic comments. The new improved dataset has been made publicly available as Ben-Misog to help the Bengali Natural Language Processing community progress. © 2024 IEEE.

关键词： Support vector machines

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm 3

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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

作者： Ninama, Keyur Patel, Jainamkumar Girish, K.K. Reddy, Manideep Raya V S R S Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment;however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm's efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis. © 2025 IEEE.

关键词： Open Data

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models 3

Identification of Genomic Associations Between Parkinson's a...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Rana, Md. Shohel Podder, Nitun Kumar Rana, Humayan Kabir Hasan, Md. Imam Azam, Md. Shafiul Rahim, Md Abdur Hasan Sazzad Iqbal, S.M. Saha, Subir Pabna University of Science and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

Neurodegenerative diseases (NDGs) can slowly damage the brain as well as the central and peripheral nervous systems. Parkinson's disease (PD) is a sensitive sensory disease that causes a malfunction of dynamic equilibrium. PD causes certain brain neuronal cells to gradually split or die. There are certain significant characteristics of PD that may increase the risk of acquiring deferent NDGs including Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Multiple sclerosis disease (MSL). We conducted a transcriptomic analysis to find connections between PD and NDGs. To explore how PD may affect the advancement of NDGs, we employed a quantitative framework to analyze the gene expression microarray dataset. To obtain genomic associations between PD and NDGs, we performed gene expression profiling by identifying dysregulated pathways, gene ontologies, PPIs, and phylogenetic analysis. We found 15, 11, 28, and 24 significant genes are shared between PD and AD, ALS, HD, and MSL respectively. We also identified a significant number of functional and ontological pathways, hub proteins and phylogenetic associations that indicate PD may have a link to develop NDGs (AD, ALS, HD, and MSL). We validate our findings through gold benchmark databases. This systematic approach is highly beneficial in understanding the causes and progression of NDGs owing to the effect of PD. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Ink Detection from Carbonized Herculaneum Papyri using Deep Learning 26

Ink Detection from Carbonized Herculaneum Papyri using Deep ...

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

作者： Mou, Farzana Sharmin Ahmed, Tanvir University of Birmingham Dept. of Computer Science Birmingham United Kingdom Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

A Roman villa in the town of Herculaneum, next to Pompeii, housed a library with thousands of ancient Greek and Roman scrolls. The eruption of Mount Vesuvius buried this villa approximately 2000 years old ago which has been discovered. The scrolls can no longer be opened without breaking them because they were carbonized by the heat of the volcano. Since Herculaneum ink is carbon-based, it affords no X-ray contrast against the underlying carbon-based papyrus. Some detached papyrus fragments that can be read under infrared light can be used as ground truth data for a machine learning model that could identify otherwise undetectable ink from X-rays. The process of ink detection involves locating the inked portions of the papyrus using data from a 3D X-ray scan of the papyrus surface. The model has been trained by selecting a pixel from the binary label picture and sampling a subvolume from the surface volume at the same coordinates. In order to update the model weights, then backpropagate the known label data. Once trained, the model can be used to predict the appearance of a label picture from diverse input data. Three distinct models, namely U-Net, Attention U-Net, and a Pretrained Autoencoder with Attention U-Net, have been applied. Then, utilizing those three models, ensemble approaches have been employed (average, voting, and weighted average) to choose the optimum outcome. The best dice score of 0.766 has been achieved by the average ensembling technique. This project shows how machine learning can detect ink in 3D scans and decipher carbonized parchment contents. © 2023 IEEE.

关键词： attention mechanism computer vision ensemble technique ink detection pretrained autoencoder unet

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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