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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Varsha, T. Shekar, Yamuna C. Shankar, Sahana P. Bharadwaj, Aryan Varadam, Deepak Supriya, M.S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

A nonprofit organization like college, works to assist all student needs and advance science, particularly in the academic sector. Hence, offering a high-quality placement aid service has emerged as a key indicator of a college's overall quality. Nowadays, in this pandemic-affected world, the majority of IT organizations are hiring students online, which makes it difficult for the college and the company to shortlist the students based on the requirements of the companies for various tasks. Due to the large number of students at colleges, the recruitment process requires a lot of human labor. We, therefore, offer a novel method for assessing job applications in online recruitment systems by using several machine learning methods to address the issue. We attempt to put into practice an application in the form of a prototype system, whose functionality is displayed and accessed in a genuine recruitment scenario. It is possible to offer a system where a computer would examine both the company's needs and the student's performance to determine whether the student should be selected for an *** analysis of student recruitment is automated in this paper utilizing machine learning methods by comparing the scores of a test with the benchmark score of the company. © 2023 IEEE.

关键词： Students

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics Research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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Classification of Cardiac Arrhythmia Using Machine Learning Algorithms 1

Classification of Cardiac Arrhythmia Using Machine Learning ...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Supriya, M.S. Vineet Kushalappa, K. Sanjay, J.R. Patnasetty, Sheetal K Bajpai, Shruti Ojha, Shubhra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348057

Irregular and unexpected slow or fast heartbeat is called arrhythmia. The two lower chambers of the heart, known as the ventricles, are where an irregular heart rhythm called a ventricular arrhythmia (VA) originates same in the case of atrial fibrillation which occurs in the two upper chambers called as aorta. An arrhythmia is a dangerous medical condition that needs prompt attention. An electrocardiogram or monitor is typically needed for its diagnosis in order to capture the irregular heart rhythms. If arrhythmia is the cause of cardiac arrest, the electrocardiogram (ECG) can help identify the cause. In this research, to investigate and evaluate a few pre-processing techniques for ECG signals. As the initial stage of pre-processing, this involves a variety of techniques to eliminate noise from the ECG signals and any undesired disturbances like Equi-ripple notch filter and Kaiser Window function. Next, several ways were investigated to turn ECG signals into pictures, segments, or waveforms that can be used as input for various machine learning algorithms like Logistic regression, Random Forest, Decision Tree and KNN classifier to analyze and forecast the likelihood of ventricular arrhythmia. Finally, a comparative analysis is done to compare all the machine learning techniques using multiple pre-processing techniques. © 2023 IEEE.

关键词： Electrocardiograms

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Smart E-Locker System using IoT

Smart E-Locker System using IoT

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Umadi, Keerti R. Aishwarya, Adoni Narendramath, Jyothi E. Inchara, S.M. Dharishini, P. Padma Priya Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665474511

In order to enhance daily life and actualize the Internet-of-Things (IoT) goal, several IoT gadgets have been created. IoT, which is based on the information-sharing capabilities of Radio Frequency Identification (RFID), is developing into one of the key technologies that is being used in a wide range of applications. Security and Privacy are necessities in today's world. Security systems are being used exponentially more frequently in our daily lives and we all know that this is an information era where all transactions are through the exchange of data. The idea behind this smart e-locker system primarily came from the asset security system utilized in bank lockers. This smart e-locker system, triggers an alarm when a user attempts to enter a guarded area by scanning an unregistered token. This system includes unique and personalized tags that allow only the authorized user to enter the secured premises. The most unique feature is that the log of the number of people approach the locker or the secured area is stored in adafruit cloud. If an unauthorized user is trying to enter the secured area by scanning the unregistered token the buzzer will be activated automatically causing the guards to be notified and an alert is send to person in-charge through e-mail. This paper demonstrates how RFID technology improves security and convenience, which will be useful in the future. © 2023 IEEE.

关键词： Internet of things

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Bridging the Communication Gap—A Pilot Course on Interdisciplinary Requirements engineering

Bridging the Communication Gap—A Pilot Course on Interdisci...

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Joint of REFSQ 2025 Workshops, Doctoral Symposium, Posters and Tools Track, and Education and Training Track, REFSQ 2025

作者： Gieshoff, Anne Catherine Ruiz, Marcela Schuler, Martin Wahler, Michael Zurich University of Applied Sciences School of Applied Linguistics Institute of Translation and Interpreting Winterthur Switzerland Zurich University of Applied Sciences School of Engineering Institute of Computer Science Winterthur Switzerland

Teaching the practical aspects of requirements engineering is challenging due to the lack of effective tools, methods, and practices for teaching requirements elicitation, especially for novices. The research community has proposed various solutions to measure the effectiveness of elicitation techniques, such as interviews. However, there is limited research on teaching experiences that address both technical and communication skills. To address this, we implemented an innovative interdisciplinary pilot course for final-year computer science and applied linguistics students at our university. Linguistics students conceptualized a language learning application, acting as the stakeholders, while computer science students elicited requirements through interviews. This collaboration provided realistic stakeholder interaction and insight into the software development process for linguistics students. This paper details the setup of the interdisciplinary pilot course, presents the course evaluation results, and shares the lessons learned from this unique educational endeavor. © 2025 Copyright for this paper by its authors.

关键词： Requirements engineering

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Prediction Based Reversible Data Hiding for Gray-Scale Images 7

Prediction Based Reversible Data Hiding for Gray-Scale Image...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Uyyala, Ravi Bojjagani, Sriramulu Department of Computer Science And Engineering Hyderabad Gandipet India School of Engineering And Applied Sciences SRM-AP Department of Computer Science And Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350371406

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior. © 2023 IEEE.

关键词： Pixels

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT Networks

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SN computer science 2024年第2期5卷 1-14页

作者： Kumar, Rakesh Singh, Sunil K. Lobiyal, D.K. Kumar, Sudhakar Jawla, Shashi Department of Applied Computational Sciences & Engineering GLBITM Greater Uttar Pradesh Noida India Department of Computer Science & Engineering CCET Panjab University Chandigarh India School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering SVIET Mohali India

Routing protocols, responsible for determining optimal paths, fall into two main categories: reactive and proactive protocols. In the realm of reactive routing protocols, exemplified by Ad hoc On-demand Distance Vector (AODV), routes are created only when there is an actual data transmission requirement. In contrast, proactive routing protocols maintain pre-computed paths to all potential destinations, resulting in reduced resource utilization within reactive protocols and continuous route maintenance within proactive ones. Reactive routing protocols are resource efficient as they establish routes as needed, while proactive counterparts maintain routing tables for all possible destinations, ensuring constant route availability regardless of data transmission demands. This paper primarily concentrates on the reactive routing protocol category, focusing on real-time path optimization and routing information updates. In the context of Vehicular Internet of Things (VIoT) networks, where malicious entities might attempt to flood, mislead, or impersonate routing packets, it is imperative to ensure robust security measures within the routing protocol. Unfortunately, secure routing protocols in VIoT networks, including AODV, SAODV, and SGHRP, often exhibit inefficiencies and impose a high overhead. To address these challenges, this research paper introduces the Security Metrics and Authentication-based RouTing (SMART) protocol for VIoT networks, with a focus on enhancing security while minimizing overhead. The SMART protocol utilizes the Merkle tree for hash (digest) generation, which is then encrypted using Elliptic Curve Cryptography (ECC) to reduce overhead. This proposed protocol enhances security by authenticating the source and incorporating security metrics into the routing information. To assess the performance of the SMART protocol, simulations were conducted using Network Simulator-2 (NS₂). The results demonstrated an improved packet delivery ratio, red

关键词： AODV Authentication ECC Hash digest Merkle Tree OBU Routing Protocol SAODV VIoT Networks

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