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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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Pc Automation Using Hand Gestures

Pc Automation Using Hand Gestures

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Purushotham, P. Bennet, Ben Sujin Vijayakarthik, P. Kiran, Ajmeera Ramakrishna Pravallika, M. Shivani, Y. Chetan, Ch. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Technology and Applied Sciences Engineering Department Oman R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

The human computer interaction platform can be implemented in a variety of different ways because to the fact that webcams and other equipment, such as sensors, are both relatively inexpensive and readily available on the market. The use of gestures as a mode of communication between humans and machines is widely regarded as the most effective approach. The employment of hand gestures as a means of communication between humans and machines or computers is particularly helpful in achieving higher levels of information conveyance. The use of hand gestures as a form of nonverbal communication is something that can be put to use in a variety of contexts. Applications that make use of hand gestures have acquired a variety of alternative methodologies, including those that are backed by sensor technology and computer vision, according to research and survey reports. Research is expanding into new territory, particularly in the areas of pattern recognition and gesture recognition. Gestures of the hand can add a new layer of complexity to the operation of contactless computers. As a result, in this project, we produce software that shows a prototype of a system that is able to automatically recognize gestures and executes pc commands in response to certain motions. This software is shown as part of this project. Python is the language that we are utilizing to construct our solution, and we are taking advantage of integrated modules such as cv2 (OpenCV, or open source computer vision, is a collection of programming functions that is primarily geared toward real-time computer vision.) & Mediapipe (detects hand and fingers data points.) © 2023 IEEE.

关键词： Human computer interaction

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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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A systematic review of decentralized finance protocols

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International Journal of Intelligent Networks 2023年第1期4卷 171-181页

作者： Shah, Kaushal Lathiya, Dhruvil Lukhi, Naimish Parmar, Keyur Sanghvi, Harshal Dept. of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Dept. of CEECS Florida Atlantic University Boca Raton United States

Decentralized finance (DeFi) has revolutionized the financial industry in recent years. Industries such as banking, insurance, and investment are experiencing a significant shift due to the growth of DeFi. The decentralized finance market is expanding exponentially, emphasizing the potential of digital currencies and decentralized platforms in providing an alternative to the traditional finance paradigm. It eliminates the need for costly intermediaries, reduces transaction fees, and increases accessibility to financial services for everyone, regardless of their geographic location or economic status. With the enormous increase in cryptocurrency investment, individuals and institutions have started to use DeFi. In this context, understanding the architecture and working mechanisms of different DeFi protocols becomes crucial in creating new and innovative products. This review paper explores various DeFi protocols, including derivatives, decentralized exchanges (DEX), lending and borrowing, asset management, and stablecoins. It analyses their internal structure and composability, providing insights into how these protocols can be combined to create new and innovative DeFi products and explore the potential of DeFi in providing an alternative to the traditional finance paradigm. © 2023 The Authors

关键词： Asset management

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification 26

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

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

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

ISBN: (纸本)9798350359015

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security. © 2023 IEEE.

关键词： Attention Mechanisms Crop Leaf Disease Identification Deep Learning DeepLeaf-Attentive MobileNet Transfer Learning

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Financial Viability of PV Grid for an Intelligent System of Residential Buildings in sub-urban Areas of Punjab, Pakistan 16

Financial Viability of PV Grid for an Intelligent System of ...

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16th Seminar on Power Electronics and Control, SEPOC 2024

作者： Shehzad, Moazzam Jamal, Nasir Muqeet, Hafiz Abdul Qureshi, Noman Sohaib Lincoln University College Dept. of Electrical Engineering Petaling Jaya Malaysia Punjab Tianjin University of Technology Dept. of Electrical Engineering Lahore Pakistan University of Engineering and Technology RCET Campus Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350379419

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study's findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan's suburban region. © 2024 IEEE.

关键词： Computational Optimization Feed-In-Tariff Net Energy Metering Net Present Value Net-Metering

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Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance Data Streams Classification

Hybrid Nature-Inspired Based Oversampling and Feature Select...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Arya, Monika Dewangan, Bhupesh Kumar Verma, Monika Rohini, M. Motwani, Anand Sar, Sumit Kumar Bhilai Institute of Technology Dept. of Computer Science & Engineering CG Durg India Op Jindal University Dept. of Computer Science Engineering Raigarh India Sri Krishna College of Engineering and Technology Dept. of Computer Science & Engineering Coimbatore India Vit Bhopal University School of Computing Science & Engineering MP Sehore India

ISBN: (纸本)9781665492942

A big data stream is described using 5 mathrm{V}primes (Volume, Variety, Velocity, Variability, and Veracity). These characteristics impose various challenges. In many cases, data streams are unbalanced, making traditional data mining approaches impossible to employ. The standard data mining approaches are not suitable for imbalanced data streams for achieving analytical efficiency because they require periodic analyses, but big data requires real-time analytics. Additionally, the induction model must be re-run and rebuilt each time to add up the most recent data. Mining these unique streams, on the other hand, is one of the most intriguing research areas. Deep learning (DL) algorithms were developed to increase classification performance for issues requiring large data sets with varying types and characteristics. Feature selection (F.S.) is a critical stage in any classification application. F.S. entails eliminating superfluous and redundant characteristics, resulting in a prediction model that is more efficient, interpretable, and fast. While complete solutions are available for F.S., managing massive data streams that need instantaneous processing is challenging by its own nature. This work provides a hybrid metaheuristic strategy with FFSMOTE for oversampling imbalanced data stream and Honey Bee algorithm for feature selection. Hybridization aims to improve feature selection processes by combing the advantage of both the algorithms. Furthermore, the Ensemble Deep classifiers are used for data stream classification. © 2023 IEEE.

关键词： Classification (of information)

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Ergonomic Factors Impacting Industrial Operator Performance, Productivity, and Musculoskeletal Health

Ergonomic Factors Impacting Industrial Operator Performance,...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Shahabadkar, Krutika Dani, Sourabh Shahabadkar, Ramesh Kulkarni, Ambarish Swinburne University of Technology Dept of Computer Science HawthornVIC Australia Swinburne University of Technology Dept of Product Design Engineering HawthornVIC Australia Amc Education Dept of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350338522

This paper investigates the intricate relationship between workplace factors and their influence on the occurrence of Work-Related Musculoskeletal Disorders (WMSDs). It delves into the factors affecting industry operators' performance, emphasizing the role of organizational, ergonomic, and individual factors as triggering elements. To quantitatively assess these factors' contributions to a person's work productivity, a novel grid model is proposed, enabling a structured evaluation. The study involved a cohort of ten actively participated in the study. Participants provided insights into workplace conditions significantly affecting WMSDs through a meticulously designed questionnaire. The mathematical model presented here adopts a systematic and analytical approach, employing algebraic equations to represent both hyperbolic and linear relations. This model establishes a structured framework for the assessment and optimization of workplace conditions, ultimately aiming to reduce the incidence of WMSDs. Parametric studies were conducted to evaluate the model's effectiveness, facilitating the prediction of different factors' impacts on the performance index linked to WMSD reduction. Results indicate that ergonomic factors affected 60% of the participants' performance. Similarly, individual factors affected 20% and organisational factors affected 50% of the participant's performance. This paper sheds light on the dynamic interplay of these factors, offering valuable insights for the prevention of WMSDs in various occupational settings. © 2023 IEEE.

关键词： Ergonomics

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Cyberbullying Text Identification: A Deep Learning and Transformer-based Language Modeling Approach

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2024年第1期11卷 1-12页

作者： Saifullah, Khalid Khan, Muhammad Ibrahim Jamal, Suhaima Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Dept. of Information Technology Georgia Southern University StatesboroGA United States Centre for Securing Digital Futures School of Science Edith Cowan University PerthWA6027 Australia

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language. Copyright © 2024 K. Saifullah et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Embeddings

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