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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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Practical Digital Transformation Framework for Small and Medium-Sized Enterprises in the UK 20

Practical Digital Transformation Framework for Small and Med...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Sirinanda, Milinda D. Balota, Josiah E. Teesside University Dept. of Student and Library Services Middlesbrough United Kingdom School of Computing Engineering & Digital Technologies Teesside University Dept. of Computing & Games Middlesbrough United Kingdom

ISBN: (纸本)9798350365856

Small and medium-sized enterprises (SMEs) are pivotal to the economy of many countries, including the United Kingdom. With the advent of digital technologies, the imperative of digital transformation has become increasingly salient for firms striving to retain competitiveness and relevance. However, SMEs lag behind larger organizations in adopting digital transformation, primarily due to the inadequacies of existing models tailored to the SME sector. Consequently, there is a palpable exigency for a pragmatic and tailored digital transformation framework specifically catered to SMEs operating in the UK. This research addresses this gap by proposing a practical digital transformation framework tailored to the unique characteristics and requirements of SMEs in the UK. Utilizing qualitative research methodologies within the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA model), existing research articles and digital transformation models have been meticulously evaluated to devise a novel framework. The resulting framework comprises six pivotal dimensions: strategy, organization, technology, innovation, culture, and change. It distinguishes itself by its adaptability, simplicity, and flexibility, allowing SMEs to tailor it according to their distinct business models and operational contexts. Despite the scarcity of literature on SME digital transformation in the UK, this research paper serves as a seminal contribution, addressing a critical gap and endowing SMEs with a pragmatic toolkit to navigate the complexities of digital transformation. © 2024 IEEE.

关键词： Digital Business Models Digital maturity Digital maturity models Digital Transformation UK SMEs

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Supply Chain Logistics With Hybrid Optimization using ADMM and Vehicle Routing Problem 1

Supply Chain Logistics With Hybrid Optimization using ADMM a...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Reddy, T. Sudeep Dhanush, D. Krithin, T. Jayan, Sarada Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350376517

Delivery routing and truck assignment for transportation resources are essential elements in the dynamic world of supply chain management and logistics. This study proposes a framework for maximising the number of trucks assigned and the delivery routes by using OR-Tools to solve the Vehicle Routing Problem (VRP) and the Alternating Direction Method of Multipliers (ADMM) for demand assignment. With consideration for each vehicle's capacity, air conditioning condition, and demand priority, we created a realistic dataset that included warehouse locations, truck specifications, and delivery demand priorities. A time matrix was created by converting a distance matrix and assuming a constant speed of movement between the warehouses. Based on compatibility restrictions including vehicle capacity, air conditioning requirements, demand priority, and overall cost reduction, the ADMM algorithm allocates delivery demands to trucks. To successfully balance these aspects, the objective function incorporates weights for cost, priority, and distance. Following demand assignment, delivery routes are optimised using OR-Tools. Time windows and vehicle capacities are included as limitations to guarantee that deliveries are made within predetermined windows while reducing travel time and distance. In addition, a web application that offers an interactive platform for managing trucks, warehouses, and needs was created utilising the Flask framework. Through an easy-to-use interface, users can examine optimised routes, add new entries, and view extensive informative dashboard. By meeting delivery deadlines and maximising resource utilisation, this integrated approach produces notable operational efficiencies. This illustrates the potential of fusing cutting-edge optimisation techniques with useful application to handle real-world logistics problems. © 2024 IEEE.

关键词： Travel time

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Oral Vision: Advanced Image Recognition for Oral Cancer 5

Oral Vision: Advanced Image Recognition for Oral Cancer

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ghosh, Joita Mishra, Khushi Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Cancer that affects the lining of the mouth, lips, or upper throat is called oral cancer. Other names for it include tongue cancer, mouth cancer, and oral cavity cancer. There is an urgent need for early cancer detection due to the rise in cancer cases in all age groups. Alcohol consumption and tobacco usage are risk factors. The risk of oral cancer is fifteen times higher for people who use both alcohol and tobacco than for people who do not. This paper presents a deep learning-based technique that uses the ResNet50 convolution neural network model to classify the images of malignant and non-malignant oral cell images. To enhance the pretrained ResNet50 model's capacity to differentiate malignant and benign samples, it was trained using a dataset of histopathological oral tissue pictures. A special virtual voice assistant has also been integrated along with the model to make it more assistive for all kinds of users. In order to track the advancement of these methods, this paper also analyses how combining NLP with point-of-care tools makes use of a variety of algorithms and sets. © 2024 IEEE.

关键词： Lung cancer

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E-Waste Alchemy: A Unified Solution for Recycling and Sustainability 5

E-Waste Alchemy: A Unified Solution for Recycling and Sustai...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sharma, Vansh Sameer, Md Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Through user-centric solutions and advanced features, E-Waste Alchemy is an innovative platform that aims to transform the administration of electronic waste (e-waste). The Recycle Centre on the platform is comprehensive, allowing users to specify the brand and model of laptops, smartphones, and a variety of accessories in order to schedule and select the retrieval. E-Waste Alchemy further improves user convenience by enabling real-time tracking of nearby recyclers based on the user's current location. Furthermore, the platform employs predictive analytics to determine the market values of the components contained within each device, including plastic, aluminum, and nickel. The responsible disposal of e-waste is promoted by this predictive feature, which also contributes to environmental sustainability by optimizing recycling processes. E-Waste Alchemy is a forward-thinking and adaptable solution that facilitates the administration of e-waste and promotes a circular economy. © 2024 IEEE.

关键词： Circular economy

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Enhancing Cartoonification using GAN Learning 2

Enhancing Cartoonification using GAN Learning

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sangapu, Sreenivasa Chakravarthi Manogna, S.V.S. Sountharrajan, S. Suganya, E. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Cse Chennai India Ssn College of Engineering Dept. of It Chennai India

ISBN: (纸本)9798350383287

Cartoons are a ubiquitous art form in day-today living, and auto-generating cartoons from photos are highly desirable. But cartoons demand smooth color shading, clear edges, and moderately basic surfaces, which pose critical challenges for texture-descriptor-based capacities as cartoonification and its styles demand intriguing characteristics with high levels of disentanglement and reflection. GAN-based learning techniques are effectively instituted for converting real-world photographs or scenic images into cartoon-style images. But not all the styles and cortoonification methods are suitable, as a few applications demand versatile generative models while others require pure cartoon-style transformations. The proposed work primarily focuses on the implementation feasibility of various GAN approaches, such as CartoonGan and StyleGan2. Further, these models are analyzed and evaluated for their utility potential and variances in different image and cartoon styles, so that suitable models are recommended for the needs of Industry 5.0 and Society 5.0 applications. © 2024 IEEE.

关键词： Computer vision

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Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. Robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape. © 2024 IEEE.

关键词： Smart manufacturing

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Harnessing the Power of Artificial Intelligence in Materials Science: An Overview

Harnessing the Power of Artificial Intelligence in Materials...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

ISBN: (纸本)9798350358155

The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future. © 2024 IEEE.

关键词： Artificial Intelligence Machine Learning Materials Characterization Materials Science

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