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检索条件"任意字段=International Conference on Emerging Trends in Electrical and Computer Technology"

共 10362 条记录，以下是11-20 订阅

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Enhancing User Authentication with a Secure Human-Computable Password Scheme 2

Enhancing User Authentication with a Secure Human-Computable...

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2nd international conference on emerging trends in Information technology and Engineering, ic-ETITE 2024

作者： Ayeswarya, S. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in the digital world has become a challenging and difficult task. The username-password combination is no longer reliable and the tools that support authentication mechanism are all breakable. In addition to authentication, to avoid bots, CAPTCHA is used which increases the complexity of the system. Thus the research is going towards making use of human brain which is so unique and powerful in the authentication process. This will also eliminate the need for CAPTCHA since human is computing the password. To make it feasible, the function that is computed by the human should be simple enough. The existing method trusts the computer to store the public images and if there is a key logger installed in the system, the adversary will be able to get the password. This again tends to be a fixed password. Instead, if it is based on a challenge from the server, the password will be dynamic and secure. In this paper a human computable password protocol is suggested. The experimental results prove that the proposed technique is efficient and is less prone to attacks. © 2024 IEEE.

关键词： Authentication

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Proceedings - 2024 Joint international conference on Digital Arts, Media and technology with ECTI Northern Section conference on electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

Proceedings - 2024 Joint International Conference on Digital...

引用

Joint 9th international conference on Digital Arts, Media and technology with 7th ECTI Northern Section conference on electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

ISBN: (纸本)9798350318241

The proceedings contain 108 papers. The topics discuss include: mobile assessment for the evaluation of media and alcohol consumption behaviors among communities in tourist districts;a survey study of ChatGPT adoption in Northern Thai OTOP skincare and herbal cosmetic;logistic cost factors of Rayong Durian export to China under regional comprehensive economic partnership;interactive packaging design and use of augmented reality technology on packaging for the elderly a case study of herbal tea products, Doi Mae Salong Subdistrict, Mae Fah Luang District, Chiang Rai;industry 4.0 and beyond – present trends, emerging solutions and future technologies in the area of the industrial automation;detection of Alzheimer’s disease using machine learning classification;and bridging gaps in elementary English classroom management: a gamification perspective.

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IoT Based Smart Economic Zone Weather Monitoring System 4

IoT Based Smart Economic Zone Weather Monitoring System

引用

4th international conference on emerging trends in Networks and computer Communications, ETNCC 2024

作者： Hassain, Md. Mehedi Huria, Jannatul Barua, Emon Delower, Humaira Arefin, Md Reyad International Islamic University Chittagong Dept. of Electrical and Electronic Engineering Chattogram4314 Bangladesh International Islamic University Chittagong Dept. of Electrical & Electronics Engineering Chittagong Bangladesh

ISBN: (纸本)9798350353266

Weather prediction has become more challenging due to changes in the weather. In industrial and urban regions, air pollution poses a serious risk to public health and the environment. This research proposes a cloud based, Internet of Things (IoT) weather reporting system. Numerous atmospheric variables, such as dust, temperature, humidity, barometric pressure, and air quality in parts per million (PPM), can be tracked and recorded. By measuring dangerous gases, the system monitors the quality of the air. The system provides an easy to use remote weather pollution monitoring system that addresses the constraints of the current setup by providing air quality data in parts per million (PPM) on a web portal, mobile app, and also demonstrating on an LCD display. The ESP8266 is used with the HTTP communication protocol to send signals. A microcontroller and SIM900A are used to send data to a cloud based server that users can access with website or a mobile app from any location. It also utilizes GSM SMS technology to send out high -alert signals based on typical meteorological data and inform the responsible government organizations, providing an affordable, low-power, and real-time air pollution monitoring solution. © 2024 IEEE.

关键词： HTTP

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Continuous Sign Language Recognition using Convolutional Neural Network 2

Continuous Sign Language Recognition using Convolutional Neu...

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2nd international conference on emerging trends in Information technology and Engineering, ic-ETITE 2024

作者： Shankara Narayanan, V. Sneha Varsha, M. Padmavathi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Automatic Continuous Sign Language Recognition is a task that aims at associating words/phrases for sign language gestures. The scope of building a CSLR framework extends beyond bridging the communication barrier between the deaf community and the hearing majority but includes profound benefits such as bootstrapping translation systems, human-computer interaction, virtual reality environments, etc. In this study, a continuous sign language recognition framework based on a CNN architecture is proposed. Utilizing the Argentina Sign Language dataset, sign language gestures are preprocessed to extract training and testing frames. Employing transfer learning with the ResNet-50 model, a CNN is trained to classify each frame into one of 64 different words present in the dataset. The paper also conducts a thorough post-training data analysis, offering insights into areas where the model performed less effectively, identifying commonly confused pairs of sign language gestures. Additionally, a heatmap overlay technique is employed to elucidate the black-box CNN model. © 2024 IEEE.

关键词： computer vision

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th international conference on emerging trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Unveiling Fake News with Machine Learning and Natural Language Processing Techniques 2

Unveiling Fake News with Machine Learning and Natural Langua...

引用

2nd international conference on emerging trends in Information technology and Engineering, ic-ETITE 2024

作者： Kavitha, S. Anchitaalagammai, J.V. Murali, S. Deepalakshmi, R. Anuradha, G. Thaya Rani, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350328202

The detection of fake news using machine learning and natural language processing techniques. With the proliferation of misinformation on social media and digital platforms, identifying fake news has become crucial. The project preprocesses textual data, applies various machine learning models, including logistic regression, Naïve Bayes, decision trees, random forest, neural networks, support vector machines, and AdaBoost Classifier, and employs dimensionality reduction techniques like PCA and t-SNE. The dataset is categorized into cases with and without speaker and political party information, and label encoding is used for numeric conversion. The results highlight the challenges and complexities of fake news detection. © 2024 IEEE.

关键词： Logistic regression

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Advancements in Hybrid Inverter technology: Design and emerging trends 10

Advancements in Hybrid Inverter Technology: Design and Emerg...

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10th international conference on electrical Energy Systems, ICEES 2024

作者： Kandaswamy, Vijay Anand Jeeva, S. Mohan Raj, S. Vishnupriyan, J. Suganyadevi, M.V. Pugazhendhi, S. R.M.K. Engineering College Dept. of ECE Chennai India Chennai Institute of Technology EEE Dept Chennai India Chennai Institute of Technology Centre for Energy Research Chennai India Saranathan College of Engg. Dept. of EEE Trichy India

ISBN: (纸本)9798350353778

Hybrid inverters, now pivotal in contemporary power systems, especially in the integration of renewable energy and microgrid applications, are thoroughly examined in this paper. It begins with an extensive overview of hybrid inverter design, control strategies, and their manifold applications. The paper elucidates the fundamental operational principles of hybrid inverters, emphasizing their efficacy in integrating diverse energy sources like solar photovoltaic (PV), battery storage, and conventional sources into a power system. Subsequently, various design considerations including topology selection, sizing, and interfacing with energy storage systems are discussed in detail. Furthermore, emerging trends in hybrid inverter technology are outlined, highlighting opportunities for further advancements in performance, flexibility, and integration with emerging energy systems. © 2024 IEEE.

关键词： Hybrid power

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Dynamic and Energy Efficient Data Traffic Scheduling for Earlier Forest Fire Detection using WSN 2

Dynamic and Energy Efficient Data Traffic Scheduling for Ear...

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2nd international conference on emerging trends in Information technology and Engineering, ic-ETITE 2024

作者： Swetha, R. Navaneethan, C. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In general, data traffic volume is significant in sensor networks, and communication often occurs with variable capacity during early forest fire detection using Wireless Sensor Networks (WSN). Initially, optimal routes are determined by selecting the best prime nodes. Subsequently, packets are dynamically scheduled based on the severity of the data traffic associated with detecting forest fires. The selection of dominant weighted nodes is a crucial initial stage, and these nodes are chosen for optimal routing, taking into consideration factors such as distance and energy criteria. The second stage involves the dynamic scheduling of packets, ensuring that they are sent based on packet priority, queue index value, and the urgency of forest fire detection. This process aims to minimize congestion and prevent unnecessary transmission delays, contributing to the efficient and timely detection of forest fires using WSN. © 2024 IEEE.

关键词： Fires

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Convolutional Neural Network driven Electroencephalogram Characterization for Robust and Efficient Schizophrenia Diagnosis 3

Convolutional Neural Network driven Electroencephalogram Cha...

引用

3rd international conference on emerging trends in electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Iqbal, Haseeb Khan, Salaar Tahir, Anoosha Ramzan, Hamza Biomedical Engineering Department University of Engineering & Technology Pakistan School of Science & Engineering Lahore University of Management Sciences Pakistan Buch International Hospital Multan Pakistan Computer Science Department University of Engineering & Technology Pakistan

ISBN: (纸本)9798331504861

Complex psychiatric disorders such as Schizophrenia show symptoms like disorganized thinking and hallucinations, typically rooted in abnormal brain activity. Early detection of such abnormal brain activity significantly improves diagnosis and subsequent treatment. To that end, Electroencephalogram (EEG), a non-invasive technique that records electrical activity in the brain is often used to record information about these cerebral anomalies. EEG data can be analyzed using various techniques to facilitate Schizophrenia diagnosis. In that respect, traditional machine learning techniques have been extensively used. However, traditional machine learning techniques necessitate pre-processing and extensive feature extraction, which can often considerably vary from one dataset to another. In contrast, deep learning techniques are better suited to classify between normal and schizophrenic cases, owing to their sophisticated network architecture and their ability to resolve complex data. In this work, we leverage a convolutional neural network (CNN) to characterize EEG data from two cohorts to facilitate schizophrenia diagnosis. We report a 96.4% accuracy of classification with fewer network parameters which beats the current gold standard and shows promise for effective and robust detection and subsequent Schizophrenia diagnosis based on EEG data. ©2024 IEEE.

关键词： Electroencephalography

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Impact of Ibuprofen on Marine Algal Metabolites Investigated Using a Coplanar Microwave Sensing Probe and Machine Learning 21

Impact of Ibuprofen on Marine Algal Metabolites Investigated...

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21st international conference on electrical Engineering/Electronics, computer, Telecommunications and Information technology (ECTI-CON)

作者： Russel, Mohammad Bingke, Yang Chalermwisutkul, Suramate Dalian Univ Technol Sch Chem Ocean & Life Sci Panjin 124221 Peoples R China King Mongkuts Univ Technol North Bangkok Sirindhorn Int Thai German Grad Sch Engn Bangkok Thailand

ISBN: (纸本)9798350381566;9798350381559

Seaweed is an important primary producer of seawater. Its existence is not only of great significance to the Marine ecosystem, but also creates economic value through algae metabolites such as lipids, sugars, and pigments. As the seaweed grows, other nutrients in the environment, as well as metabolites from other Marine organisms, cause the algae to produce some natural pigments that have high nutritional value for the human body. However, in recent years, the identification of a class of pollutants known as emerging pollutants (EPs) has brought attention to their role as significant water contaminants, adversely impacting the endocrine systems of both humans and wildlife. When these pollutants enter the sea water, they affect the growth of seaweed and indirectly cause the change of algae metabolites. Consequently, there will be a variety of secondary pollutants that complicated the understanding of their impact on marine ecosystems. Thus, there is an urgent need to develop smart and intelligent techniques to monitor the EPs and their impacts on marine organisms. Dielectric spectroscopy can quickly and accurately obtain information regarding the material properties, so as to realize the non-destructive and rapid measurement of the samples. Thus, this method is more and more favored by researchers. In this paper, a customized one port PCB based CPW probe for dielectric spectroscopy is proposed to study the influence of ibuprofen as an example of emerging pollutants on marine algae growth activity. Leveraging machine learning in conjunction with traditional techniques, we analyze the impact of different concentrations of ibuprofen on marine algae growth activity in this work.

关键词： emerging pollutant Ibuprofen Dielectric spectroscopy Marine algae Machine learning PCB based CPW probe

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