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Next Generation Intelligent Traffic Signal Control: Empowering Electronics Consumers with Edge-AIoT Capabilities

IEEE Transactions on Consumer Electronics 2025年第1期71卷 1926-1934页

作者： Chavhan, Suresh Doswada, Rohit Gupta, Deepak Gunjal, Saymam Rodrigues, Joel J.P.C. School of Data Sciences Indian Institute of Science Education and Research Kerala Thiruvananthapuram India C-DoT Karnataka Bangalore India Dept. of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Centre for Research Impact and Outcome Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Karnataka Bangalore India Federal University of Piaui PI Teresina Brazil

Traffic congestion has become a major issue that is being faced by the majority of road users. The increasing vehicle usage, and the lack of space and funds to construct new transport infrastructure, further complicates the issue. In this scenario, it is important to come up with an intelligent and economical solution that improves the quality of road users' service. The problem with the traffic handling framework is signal timings are fixed which is not adaptive to the density of vehicles. To address this issue we propose an Edge-Augument Artificial Intelligence of Things (AIoT) road user cooperation for traffic management. The proposed system efficiently utilizes electronic devices to learn and adapt to changing traffic conditions in real-time. By optimizing the traffic signal timings based on the actual traffic conditions, adaptive systems reduce delay, improve traffic flow, reduce fuel consumption and pollution, and improve the electronics consumers' and road users' experiences. The proposed system has been tested with real-time experiments by integrating Electronic devices like cameras, smartphones, and AGX Xavier (edge device) with Cloud (ThingSpeak). The proposed system is verified by simulating the proposed system in the SUMO traffic simulator and its reliability is concluded by comparing the waiting time, depart delay, running, and halt time with the existing traditional method. © 2025 IEEE.

关键词： Traffic congestion

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Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework 26

Detecting Racist Text in Bengali: An Ensemble Deep Learning ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Saruar Jahan, S.S. Jahan, Nusrat Priota, Sadia Rahman Ahsanullah University of Science & Technology Dept of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Racism is an alarming phenomenon in our country as well as all over the world. Every day we have come across some racist comments in our daily life and virtual life. Though we can eradicate this racism from virtual life (such as Social Media). In this paper, we have tried to detect those racist comments with NLP and deep learning techniques. We have built a novel dataset in the Bengali Language. Further, we annotated the dataset and conducted data label validation. After extensive utilization of deep learning methodologies, we have successfully achieved text detection with an impressive accuracy rate of 87.94% using the Ensemble approach. We have applied RNN and LSTM models using BERT Embeddings. However, the MCNN-LSTM model performed highest among all those models. Lastly, the Ensemble approach has been followed to combine all the model results to increase overall performance. © 2023 IEEE.

关键词： BERT NLP racism text-analysis wordembedding

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Detection of Various Plant Leaf Diseases Using Deep Learning Techniques 2

Detection of Various Plant Leaf Diseases Using Deep Learning...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Harshavardhan, K. Krishna, P J V Abhinav Geetha, Angelina Hindustan Institute of Technology and Science Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

To sustain healthy crop yields and avoid extensive plant disease damage, it is essential to diagnose plant leaves. Traditional diagnostic techniques, however, can be laborious and complicated. Deep learning methods like ResNet 34 have recently come to light as a potential strategy for automating plant leaf diagnosis. Deep convolutional neural network ResNet 34 is capable of correctly classifying photos of plant leaves based on patterns and characteristics. A ResNet 34 model may be taught to recognize different leaf illnesses and other anomalies with excellent accuracy by training it on a huge dataset of tagged plant leaf photos. The ResNet 34 model's output may be seen on a website, enabling users to upload pictures of plant leaves and get a diagnosis in a matter of seconds. Also, the website might include details on the particular ailment or issue found, along with suggested remedies and prevention actions. The way of identifying and treating plant illnesses may be completely altered by this method of diagnosing plant leaves, which would ultimately result in healthier crops and a more sustainable future. Farmers, agronomists, and other stakeholders will find it simpler to recognize and effectively treat plant diseases thanks to the usage of ResNet 34 and a website for output display, which offers an accessible and user-friendly interface for plant diagnostics. © 2023 IEEE.

关键词： Diagnosis

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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A Detailed study on Smart Vulnerability Scanning Stability and its Issues

A Detailed study on Smart Vulnerability Scanning Stability a...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Upadhyay, Ekta Savant, Preeti Dev Bhoomi Uttarakhand University Dept of Computer Science and Engineering Uttarakhand Dehradun India Department of Computer Science and It Bangalore India

ISBN: (纸本)9798350322309

Since most industries, including manufacturing, medicine, travel, and agriculture, are paying careful attention to Technology smart sensors at the moment. To increase the security of the data under surveillance, monitors are incorporated. Therefore, it is crucial to ensure that the system is very reliable. The following subsystems - sensors, inverters, chipsets, actuators, data networks, and gadgets cooperate for an Ot remote monitoring to exist. The authors' goal is to highlight the many dependability issues and difficulties that these systems provide. Additionally, it is expanded to describe or rather dependability using functional commitment areas. A very high degree of gear, electronics, technology, and Bluetooth suitability and connection is required for the subsequent transmission of location data to the final level of computing in order to carry out the desired *** uses of Iot - based smart Measurement Techniques in the sphere of Hospital;Economic;and Cultivation domains are explored in order to analyses the technical hurdles and complication of so system components from safety standpoint. These apps include the Battery Heart Rate Monitor, the Intramuscular Drip Tracking System, or the Proactive Malware Detection was using to identify animal and bird attacks in paddy fields. © 2023 IEEE.

关键词： Internet of things

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Addressing Network Security Concerns 3

Addressing Network Security Concerns

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Rawat, Urvashi Karthikeyan, M.P. Dev Bhoomi Uttarakhand University Dept of Computer Science and Engineering Uttarakhand Dehradun India Department of Computer Science and It Bangalore India

ISBN: (纸本)9798350399264

Safety is the absence of possible environmental sources' damage. Here, we're focusing on cybersecurity, which guards against external enemies known as attacks. More individuals are connecting to the Internet, and around half of globe may view your router, networks, including smart phones without even any security measures. When there is no protection, your info is publicly accessible and anybody may view it. IPsec prevents any attacks and gives information about objects attempting to connect to your connection and if they have changed. © 2023 IEEE.

关键词： Cybersecurity

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Convolutional Neural Networks based Enhanced Forest Monitoring System for Early Fire Detection 7

Convolutional Neural Networks based Enhanced Forest Monitori...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Chatragadda, Adarsh Chalasani, Sai Harsha Vardhan Challa, Nagasupriya Gupta, N. Venkata Ramana Oleti, Prasanna Pravallika Amarendra, K. Prasad. V. Potluri Siddhartha Institute of Technology Dept. of Computer Science Engineering Vijayawada India Koneru Lakshmaiah Education Foundation Dept.of Computer Science Engineering Vijayawada India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The vital reason behind environmental damage, species extinction, contaminated air is Forest fires. An efficient monitoring system can help us to detect and prevent forest fires such that the damage is prevented. Because wildfires are harder to identify in their initial stages, a quicker and more precise detection approach might help decrease the incidence of damage they did. This research study describe a strategy for automated and advanced fire detection built on such systems with greater level of dependability and require no service or human contact. The proposed study focuses on demonstrating variety of algorithms that are used for the wildfire detection and provide the best algorithm that can be as effective as a resultant. Nevertheless, this research work reviews some of the key deep learning methods such as CNN, VGG19 and DenseNet that can be used to build a forest fire monitoring system. © 2022 IEEE.

关键词： Fires

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language 2

BanglaGPT: A Generative Pretrained Transformer-Based Model f...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Salim, Md. Shahidul Murad, Hasan Das, Dola Ahmed, Faisal Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Chattogram University of Engineering Technology Dept. of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350358667

Natural Language Processing (NLP) has entered a new era with the advent of pre-trained language models, paving the way for constructing robust language models. Pretrained transformer-based models such as GPT-2 have become prevalent due to their cutting-edge efficiency. However, these approaches rely heavily on resource-intensive languages, forcing other languages to adopt multilingual frameworks (mGPT). The mGPT model could perform better for low-resource languages such as Bangla because the model has been trained on a diverse dataset spanning multiple languages. Recent studies show that the language-specific GPT model outperforms the multilingual mGPT model. In this research, we have proposed a pretrained monolingual GPT model called BanglaGPT using the objective of causal language modeling (CLM). Due to the lack of available large datasets for NLP tasks in Bangla, we have created a Bangla language model dataset called BanglaCLM using a 26.24 GB Bangla corpus scraped from several public websites. We have used a subword-based tokenization algorithm named Byte-Pair Encoding (BPE) for Bangla and finally trained the Bangla-GPT2 model from scratch using the BanglaCLM dataset. Our pretrained BanglaGPT provides state-of-the-art performance for Bangla text generation with a perplexity score of 2.86 and a loss score of 0.45 on the test set. © 2023 IEEE.

关键词： Large dataset

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HAC-Bchain: A Secure and Scalable Blockchain-Shard Based E-Voting System

HAC-Bchain: A Secure and Scalable Blockchain-Shard Based E-V...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Joni, Sohel Ahmed Rahat, Rabiul Tasnin, Nishat Ghose, Partho Gaur, Loveleen Bangladesh University of Business and Technology Dept of Computer Science and Engineering Bangladesh Taylorâs University School of Computer Science Malaysia

ISBN: (纸本)9798350384659

Voting is a fundamental right of citizens in a democratic country and crucial for any thriving democracy. Reliable voting systems are essential for free and fair elections in the modern era. Biometric Electronic Voting Machines (EVMs) address many issues with paper-ballot systems, but their closed-source nature undermines voter trust. Traditional election systems are also vulnerable to cyberattacks. In this paper, we propose a hybrid blockchain-based electronic voting system (HAC-Bchain) to address the limitations of conventional e-voting systems and ensure a secure, auditable, tamper-proof, transparent, and privacy-preserving voting process. A scripting system for our blockchain facilitates a limited set of predefined operations for each layer, which helps authoritative figures to manage the election securely. We also combine a category-based sharding mechanism with the HAC-Bchain hybrid approach to create more data-concentrated shards, which improves scalability, performance, and data availability. Furthermore, we compare and discuss the performance and efficiency of different sharding configurations. Our experiments shed new light on the overall security, performance, and scalability of blockchain-based evoting systems. © 2023 IEEE.

关键词： Blockchain

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