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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Single-Frequency Self-Alignment RF Resonant Beam for information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2014 IEEE.

关键词： Inductive power transmission

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. The impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. This study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. These findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Impact of Data Poisoning Attack on the Performance of Machine Learning Models

Impact of Data Poisoning Attack on the Performance of Machin...

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International Conference on Data science and Network Engineering, ICDSNE 2023

作者： Das, Dipan Roy, Sharmistha Sahoo, Bibhudatta Department of Computer Science & Engineering NIT Rourkela Rourkela India Faculty of Computing and Information Technology Usha Martin University Ranchi India

ISBN: (纸本)9789819967544

The twenty-first century has witnessed widespread adoption of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). These techniques have provided reliable solutions in various areas, including statistics, information theory, and mathematics. Given the prevalence of ML techniques, there exist various adversaries which question the robustness of ML models. Adversaries aim to manipulate models to their advantage, reducing their performance and accuracy. Data poisoning attack is one such adversary in which the attacker manipulates models by introducing specially crafted poisoned data into the training dataset. This paper presents the performance analysis of different machine learning models with and without the influence of data poisoning attack to predict the probability of diabetes and its effect on accuracy and precision. It has been observed that the SVM (RBF) classifier performs best on clean data, while the KNN classifier is highly affected by data poisoning, with a lesser impact on the SVM (Linear kernel) classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reliability analysis

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Performance Evaluation of LoRa 923 MHz for the Internet of Things 10

Performance Evaluation of LoRa 923 MHz for the Internet of T...

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10th IEEE information technology International Seminar, ITIS 2024

作者： Juledi, Angga Putra Huda, Moh. Khoridatul Dwi Putra, Muhammad Taufik Delsi Samsumar, Lalu Prasetyo Adi, Puput Dani Nurdiansyah, Yanuar Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu City Indonesia Malang East Java Malang Indonesia Universitas Pendidikan Indonesia Department of Computer Engineering Bandung Indonesia Universitas Teknologi Mataram Department of Information Technology Mataram City Indonesia Research Center for Telecommunication Bandung Indonesia University of Jember Faculty of Computer Science Indonesia

ISBN: (纸本)9798331521295

This research focuses on enhancing the performance of data transmission in the Internet of Things (IoT) by leveraging LoRa technology. Specifically, it utilizes the LILYGO 923 MHz LoRa32 board, which integrates the ESP32 microcontroller with a LoRa module and additional devices like a 0.96-inch OLED display. This board also features a serial converter interface, simplifying the programming process, and uses a micro USB connection for both power and uploading code. One of the goals of this research is to enable the transmission of images using LoRa, though currently, only image conversion during transmission is implemented. The study includes reliability testing of the LoRa module over a distance of 10 km. The results show that the Packet Delivery Ratio (PDR) for Spreading Factor (SF) 7 is 50%, while for SF 12, it improves to 85%. Additionally, the Received Signal Strength Indicator (RSSI) measurements at a distance of 1000 meters with 3 Non-Line-of-Sight (NLoS) conditions yielded values of -120 dBm, -90 dBm, and -78 dBm. These results highlight the potential of LoRa technology in improving IoT data transmission, especially for long-range applications. © 2024 IEEE.

关键词： Image enhancement

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Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique 21

Accurate Classification of Cervical Cancer Based on Multi-la...

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21st International Learning and technology Conference, L and T 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. Youssef, Rasha Y. The Higher Institute for Management and Information Technology Minya Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt Effat University College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Cancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods. © 2024 IEEE.

关键词： Diseases

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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Generalized Parabola Chaotic map for Pseudorandom Random Number Generator 26

Generalized Parabola Chaotic map for Pseudorandom Random Num...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Jiteurtragool, Nattagit King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok10800 Thailand

ISBN: (纸本)9791188428120

In this paper, a generalized form of chaotic map based on nonlinear function with parabolic shape is introduced. The study involves the investigation of chaotic dynamics in terms of apparent in time-domain, and both qualitatively and quantitatively examination using bifurcation diagram and Lyapunov exponents. Furthermore, the practical application of these parabolic chaotic maps is showcased in a pseudorandom number generator, with its performance evaluated using statistical tests from the NIST SP800-22 test suite. © 2024 Global IT Research Institute - GIRI.

关键词： Lyapunov methods

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