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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Shri Vaisalini, L.C.K. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

A major problem especially in urban areas is traffic congestion which causes increased travel times, pollution, and compromises safety. This paper presents the design of the Smart Traffic light timing Management System, optimized in real-time traffic light timings using reinforcement learning techniques. This system scans data from different sources, including vehicle count in a network and waiting time of the vehicle at a traffic stop, which are utilized to continuously to alter traffic signals based on current situations. Utilizing reinforcement learning algorithms, the system continuously learns and adapts to changing traffic scenarios, striving for minimal congestion. The simulations run in SUMO (Simulation of Urban Mobility) indicate a noticeable reduction in wait times and improved flow across roads. The results form the basis for the inclusion of advanced data analytics in traffic management: it represents an effective method of mitigating urban congestion problems. The study concludes that by leveraging real-time data and machine learning, the traditional traffic systems can be transformed into an intelligent responsive network, capable of enhancing the efficiency and safety of urban transportation. © 2025 IEEE.

关键词： Traffic congestion

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A Proactive Crowdsourcing Framework for Fake Base Station Detection and Avoidance 15

A Proactive Crowdsourcing Framework for Fake Base Station De...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Saad, Omar A. Etman, Ahmed A. Abdel-Malek, Mai A. Azab, Mohamed Alexandria University Faculty of Engineering Department of Computer and Communications Engineering Alexandria Egypt The University of Arizona Department of Electrical and Computer Engineering AZ United States Virginia Military Institute Department of Computer and Information Sciences VA United States

ISBN: (纸本)9798331507695

With next-generation communication technologies becoming more integrated into critical infrastructure, securing these networks against sophisticated threats has become paramount. One of the most persistent threats to this communication is the growing presence of fake base stations (FBS). These FBSs are malicious entities that pose as legitimate base stations to intercept, manipulate, or exploit mobile communications, leading to serious privacy breaches. While some existing literature addresses the detection of such malicious entities, detecting FBSs has traditionally been the responsibility of network operators, who rely on tools like signal strength analysis and radio frequency (RF) monitoring to identify unusual patterns or rogue signals. However, these centralized methods often struggle to detect FBSs in complex environments characterized by high mobility, dense urban areas, and sophisticated spoofing techniques. This paper proposes a scalable and pervasive blockchain-based crowd-sourcing mechanism to enable extensive device participation in identifying FBSs. In a fully automated and distributed manner, the proposed platform uses vetted and verified information recorded on the blockchain to identify and report suspicious FBSs in the area. This allows other users to dissociate from these malicious entities automatically. A reputation management system is also employed to ensure the reliability and accountability of participating users. Using qualitative and quantitative evaluations, the proposed approach demonstrated effectiveness and efficiency in achieving all targeted goals. © 2025 IEEE.

关键词： Crowdsourcing

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Light Weighted YOLO Vehicle Tracker for Toll Gate Using Centroid Algorithm 3rd

Light Weighted YOLO Vehicle Tracker for Toll Gate Using Cen...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Singh, Pankaj Pratap Prasad, Shitala Kalita, Gyanjyoti Das, Jahnu Mahanta, Pallab Jyoti Department of Computer Science and Engineering CIT Kokrajhar Assam India School of Mathematics and Computer Science IIT Goa Goa India

ISBN: (纸本)9789819730896

This paper focuses on vehicle detection at highway toll gates. The study involves the detection of vehicles within a custom dataset, which comprises four vehicle classes: car, truck, bus, and motorcycle. The YOLOv4 model is utilized for training and is complemented by the implementation of the Centroid tracking algorithm. In the proposed vehicle track dataset, each class comprises 1500 annotated images for training and 300 images in the testset. These images are sourced from the Google Open Images V6 dataset, and the OIDv4 toolkit is employed to scrape dataset images. The primary objective of this study is to train a vehicle detection model and evaluate the effectiveness of various tracking algorithms, with a specific focus on the centroid tracker for real-time application at the toll gates. Importantly, this model is designed to operate efficiently on conventional and cost-effective GPUs and demonstrates a commendable level of precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal 2

Optimizing Waste Management: Integrated Pollution Detection ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Samuel Prabakaran, M. Sophia, S. Sophas Samuel, S. Karunya Institute of Technology and Sciences Department of Computer Engineering Coimbatore India Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems. © 2024 IEEE.

关键词： Data acquisition

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Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

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computer Systems science & engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud Sultan Aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing sentiment analysis social media analytics support vector machine Twitter application programming interface

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Offline writer identification using a developed deep neural network based on a novel signature dataset

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12425-12441页

作者： Keykhosravi, Davood Razavi, Seyed Naser Majidzadeh, Kambiz Sangar, Amin Babazadeh Department of IT and Computer Engineering Urmia Branch Islamic Azad University Urmia57169-63896 Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz51666-16471 Iran

Signature is used as an important, typical means of identification in banking, security controls, certificates, and contracts. Moreover, it has an intensive role in legal issues that need to be studied, considering the development of related applications. From this standpoint, this study proposes a new method for identifying offline signatures under different uncertainties such as various experimental conditions and environmental noises using deep learning approaches. To achieve this objective, a comprehensive right-to-left signature dataset based on relevant standards is collected from 85 participants at various time intervals under different experimental conditions. A developed deep neural network based on transfer learning networks is designed to extract features from raw data in a hierarchical manner. One of the benefits of the proposed method is that it is independent of right-handed or left-handed people and can be applied to both. The method proposed is examined not just on the collected dataset but also on a variety of other datasets. The proposed network has a 99% accuracy for author signature classification and can withstand a wide range of SNRs. As a result, the classification accuracy at 15 dB remains above 90%. The study's findings show that the proposed network can learn features hierarchically from raw signature data and achieve greater accuracy than other methods. Because of its superior performance, the proposed model can be used as an assistant by signature experts in a variety of applications, including the detection of forgery and criminals. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Protecting Recommendations: Combating BIAS and Manipulation in Recommendation 8

Protecting Recommendations: Combating BIAS and Manipulation ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Infant, P.Alwin Kumar, P.Mohan Jainish, G.R. Hindustan Institute of Technology & Sciencce Department of Computer Science and Engineering Chennai India Loyola Icam College of Engineering & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Recommender systems assist consumers in navigating the deluge of information by helping them find services and goods. The effectiveness of recommender systems has been thoroughly examined in research currently available from a number of angles. In the research community, robustness against malicious attack is becoming a more important concern. Collaborative Filtering (CF) recommender systems have been established to accomplish this objective. However, due to the fact that they are collaborative recommender systems are open to attack. To weaken the system, attackers can introduce fictitious and biased profiles. An examination of the random attack, Bandwagon attack, Mixed Attack and Noise Injection on recommender systems is presented in this work. Numerous algorithms are susceptible to profile injection attacks. Some of the earlier approaches suffer from only looking at one aspect of user rating values while assessing the rating matrix, ignoring alternative viewpoints. First, a rating matrix in this research that takes into account both genuine and fictitious user ratings is created. Next, using user rating time as a basis, the rating matrix is examined. Next, build a model to distinguish between real and fraudulent users using a variety of classifiers, including Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), and Bayesian Networks (BN). Ultimately, a number of tests are conducted, and a summary of the findings comparing different approaches' abilities to detect small-scale attacks and detect overall attacks is presented. © 2024 IEEE.

关键词： Collaborative filtering

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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On Improving Solar Forecasts with Enhanced IR Imaging and CNNs 26

On Improving Solar Forecasts with Enhanced IR Imaging and CN...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Nijhum, Ifran Rahman Hossain, Md Yearat Rakib, Md Mahbub Hasan Dev, Soumyabrata The ADAPT SFI Research Centre Ireland School of Computer Science University College Dublin Ireland Department of Electrical and Computer Engineering North South University Bangladesh

ISBN: (纸本)9781837242672

Renewable energy such as solar power has been widely adopted lately due to its sustainability and cost-effectiveness. However, due to the intermittent nature of photovoltaic systems, accurate solar forecasting is vital for efficient integration into the grid. In this paper, we propose a novel framework for effective solar irradiance estimation from infrared sky images. The proposed method utilizes contrast-limited adaptive Histogram Equalization (CLAHE) and OpenCV colormaps for pre-processing low-contrast and low-resolution infrared sky images. Additionally, it introduces an Inception module-based CNN-Regression model to get rich feature information and avoid feature loss. We perform a detailed comprehensive study to evaluate the effectiveness of the proposed method. While the baseline CNN regression model (without enhancement and Inception module) achieves an RMSE of 81.30 W/m2, employing the proposed technique reduces the loss to an impressive 47.85 W/m2. With a novel approach to address the existing issues of infrared sky images, the research advances the field of solar power estimation and forecasting for seamless integration into grids and residential settings. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Solar irradiance

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