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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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A Comprehensive Review on Data Visualization Techniques for Real-Time information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and Automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

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9th International Conference on information technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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Implementation of Summary-Connection-Prediction (SCP) Instructional Strategy in Flipped Classroom with Moodle: Investigate Preservice Teachers' Acceptance and Beliefs 23

Implementation of Summary-Connection-Prediction (SCP) Instru...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Hariyanti, Uun Batita, Maurish Sofie Rahmi Suharsono, Aswin Department of Information System Faculty of Computer Science Universitas Brawijaya Indonesia

ISBN: (纸本)9798400708503

This study investigated the implementation of summary-connection-prediction (SCP) instructional strategy on students' report tasks before the class under the flipped classroom learning design with Moodle. A total of 106 undergraduate students who enrolled in the Teaching and Learning Theory course participated in this study. A questionnaire based on the technology acceptance model (TAM) and open-ended interview questions were used to collect the data. Following the results, it is known that the preservice teachers have high positive acceptance of SCP strategy. Further, perceived ease of use and perceived usefulness significantly predict behavioral intention, which follows the relationship direction in TAM. Regarding the thematic analysis of interview data, three themes are elaborated to obtain in-depth information about what preservice teachers' beliefs when implementing this strategy. © 2023 ACM.

关键词： Forecasting

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Analyzing processing time and load factor: 5-node mix network with ElGamal encryption and XOR shuffling

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International Journal of information technology (Singapore) 2024年第7期16卷 4589-4597页

作者： Naikoo, Fayaz Ahmad Ahmad, Khaleel Ahmad, Khairol Amali Bin Department of Computer Science and Information Technology Maulana Azad National Urdu University Faculty of Engineering National Defence University of Malaysia

To provide anonymous communication, this paper proposes the implementation of a 5-node mix network using ElGamal encryption and XOR Shuffling. An algorithm for a 5-node mix network with XOR shuffling has been presented. The study carefully examines the processing time at each node and determines the load factor linked to each node using MATLAB simulation. By utilizing a shuffling technique to break the connection between nodes, the mix network successfully achieves communication anonymity. The effectiveness of this approach in maintaining anonymity and controlling communication load is persuasively shown by the data that are presented. The knowledge of mix networks and their function in protecting user privacy in digital communication systems is enhanced by this research. This study establishes a platform for future research and optimization of mix network deployments by offering empirical proof of its efficacy. These developments could improve digital communication networks’ security and privacy in a number of situations. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anonymous communication ElGamal encryption Mix network XOR shuffling

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Enhancing E-Learning Interactivity with Haar Cascade User Detection 8th

Enhancing E-Learning Interactivity with Haar Cascade User De...

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8th International Conference on information system Design and Intelligent Applications, ISDIA 2024

作者： Yousuf, Mohd Wahid, Abdul Khan, Mohammed Yousuf Department of Computer Science & Information Technology SoT MANUU Hyderabad India

ISBN: (纸本)9789819748945

User identification is combined with E-learning platforms has a lot of promise to improve interaction and participation in online learning settings. The implementation of a user detection system using the machine-learning technique Haar Cascade is the main topic of the paper. The strategy entails gathering and getting ready the data, training the classifier, and incorporating it into the E-learning platform without any interruptions. The use of real-time detection and tracking capabilities enables dynamic user presence monitoring. The system may be fine-tuned to handle false positives or negatives because it is made to be adaptive. Compliance with applicable laws and privacy protections resolve privacy issues. An innovative method to boost user interactivity and engagement is the integration of user identification technology based on the Haar Cascade into E-learning platforms. The main goal of this paper is to employ the Haar Cascade method to provide real-time user recognition and enable dynamic responses based on their presence. The E-learning experience is improved by seamlessly integrating this technology, creating a more individualized and dynamic learning environment. Purpose of the article to change the E-learning by creating a more personalized and engaging learning experience for users through this creative application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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CyberCure - Malware Defense system 4

CyberCure - Malware Defense System

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4th International Conference on Innovations in computer science, ICONICS 2024

作者： Shahid, Sardar Zohair Bangash, Muhammad Anas Hussain, Muhammad Rayyan Arshad, Saadia NED University Computer Science and Information Technology Department Karachi Pakistan

ISBN: (纸本)9798331504342

In today's world, the rise of cyber threats highlights the need for solutions that protect sensitive information and uphold system integrity. CyberCure, that is Pakistan's first malware defense system, is designed to meet the demands of cybersecurity by employing a comprehensive and adaptable approach to computer security. With the use of machine learning algorithms, signature based detection, and behavioral analysis, CyberCure offers a powerful defense that responds to both new and emerging threats. CyberCure offers an array of protective features, including four types of scans: quick scan, deep scan, removable device scan, and directory scan. Each scan type is specifically tailored to address different security needs, allowing users to select the proper level of analysis based on the perceived threat or the depth of investigation needed. Along With these scan options is a quarantine feature that safely isolates suspicious files, giving users the ability to remove, recover, or allow files based on a personalized risk assessment. Designed with a simple interface, users of all technical backgrounds can manage their security settings without sacrificing system performance. By improving resource usage, the software minimizes system slowdowns, allowing for a seamless balance between robust protection and everyday functionality. This paper presents CyberCure as a potential meaningful change in strengthening cybersecurity defenses, both within Pakistan and beyond. Its design and features position it as a reliable and effective antivirus solution capable of addressing the growing challenges posed by cyber threats. The insights offered here contribute to the broader conversation around developing advanced antivirus systems that can continuously adapt to the ever-changing landscape of cybersecurity, ensuring the safety of digital assets. © 2024 IEEE.

关键词： Cyber attacks

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Peer-to-Peer Energy Trading Framework Using Markov Chain for a Resilient Smart Grid

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IEEE Transactions on Industry Applications 2025年第3期61卷 4499-4514页

作者： Arora, Mayank Vishwanath, Gururaj Mirle Sharma, Ankush Chilamkurti, Naveen Department of Electrical Engineering IIT Kanpur Kanpur India Department of Computer Science and Information Technology La Trobe University - Melbourne Campus Melbourne Australia

This paper presents a resilience-driven framework leveraging advanced control technologies, particularly a Markov chain approach, to enhance the robustness of peer-to-peer (P2P) energy trading networks under Low Probability High Impact Events (LPHIE) such as climate change, cyberattacks, and natural disasters. The proposed framework addresses critical challenges in maintaining energy system stability by integrating renewable energy sources, optimizing energy exchange, and quantifying system resilience through novel metrics. A key contribution lies in the development of a multi-layered strategy that incorporates fuzzy logic for dynamic fault mitigation and a three-tier energy management system combining solar PV, battery storage, and electric vehicles. Additionally, the framework utilizes blockchain technology to ensure transparency, security, and fairness in energy trading, while promoting inclusivity and equity among diverse prosumer groups. By advancing the state-of-the-art in resilience modeling, this study provides a robust foundation for sustainable energy management in decentralized systems, offering practical insights into mitigating the impacts of LPHIE and advancing the democratization of energy resources. © 1972-2012 IEEE.

关键词： Smart power grids

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