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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Narmatha, R. Prakash, M. Vennila, V. Tamil Nadu Dharmapuri India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

In this modern world due to Road traffic, many people are unable to reach their destination at the correct time. For example, if a person needed to reach the hospital in critical condition due to road traffic, they are unable to reach the hospital and got stuck in traffic which will lead to death. We lost a lot of people because of this road traffic. So, Road Safety is the most important part of our daily life. Conjointly, this prediction accomplished enhanced the safety and security of women empowerment which acted as a security guard for people especially women, protecting themselves in every circumstance. Here, statistical analysis of road traffic networks of selective locations is implemented to prognosticate road traffic. Alert and divert the people who were identified in the traffic zone and found the location of the people who were identified in the unsafe zone. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Hospitals

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Integrated Women’s Security System with Safe Route Navigation and Instant Law Enforcement Reporting

Integrated Women’s Security System with Safe Route Navigati...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： A. Chinnasamy Aryan Donde Dhruv Joshi Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Campus Chennai

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Designed to empower women in many daily situations, the proposed integrated women's safety app is a comprehensive smartphone software that tackles safety concerns. This program offers a complete safety solution by combining various features and technologies. Users may quickly notify approved emergency contacts of their whereabouts by using the app's built-in emergency response system. Furthermore, it integrates with the city government's emergency services, facilitating seamless collaboration during critical circumstances. Users can stay safer when doing things like late-night commuting or traveling alone thanks to the app's real-time GPS tracking, which they can then share with trusted friends. Additionally, there is a panic button on the app that, when pressed, instantly contacts emergency services. Effective crisis prevention and management is possible with the aid of the software's self-defense classes, safety guidance, information on nearby safe zones, and hotlines. In addition, the app's real-time event reporting feature allows women to document and discuss incidents of harassment or assault, which promotes responsibility and provides politicians with valuable data. Improving women's safety, decreasing reaction times to emergencies, and providing a solid support system are all goals of the integrated women's safety app. This application integrates several safety features and emergency response systems, making it a powerful tool to safeguard women's interests and welfare in modern society.

关键词： Law enforcement Social networking (online) Urban areas Emergency services Solids Real-time systems Software

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Dual-path Residual UNet with Convolutional Attention based Swin-Spectral Transformer Network for Segmentation and Detection of Ovarian Cancer

Dual-path Residual UNet with Convolutional Attention based S...

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Advanced computing Technologies and Applications (ICACTA), International Conference on

作者： A Shanthini Arathi Boyanapalli Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai

In this paper, a new transformer based deep learning network (TDLN) model has designed for detecting ovarian cancer (OC) from the input samples. Primarily, the input images are pre-processed to remove the unwanted noises and thereby enhances the quality through HSV color channel conversion, block matching with triple dimensional filtering (BM_TriD-Fil) and improved gabor wavelet transform (IGWT). From the pre-processed images, the affected regions are gets segmented through a new dual-path residual UNet (DPResU-Net) structure. Using the segmented regions, the feature extraction and classification is performed by utilizing a novel convolutional adaptive-attention based swin-spectral transformer network (CA 2 _S 2 TNet) model. In order to tune the parameters of CA 2 _S 2 TNet model, chaotic fire hawks optimization (CFHO) has employed. The proposedTDLN model is implemented in the python platform through TCGA-OV dataset and assessed the performance in terms of different evaluation measures. The maximum classification accuracy obtained by TDLN is 99.91%, superior to the existing approaches for OC classification.

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From Text to Visuals: Advancements in Depression Prediction Using AI and Machine Learning Techniques

From Text to Visuals: Advancements in Depression Prediction ...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： S. Annapoorani P. Saravanan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulatur India

Social lifestyle and COVID pandemic situation to produce a direct impact on mental health. Depression remains a primary cause of disability worldwide and contributes significantly to the global health burden. It can typically lead to suicidal ideation and have a negative impact on anyone's professional and personal life. Artificial intelligence, machine learning, and deep learning techniques can forecast depression in a timely manner utilizing a variety of resources. Different kinds of sources, such as surveys, social networking sites, video images, and speech, can be used to accurately identify depression without the assistance of a psychiatrist. People on social networking sites express their feelings through appropriate text, which may be examined using machine learning algorithms. Through visual and speech data, deep learning approaches properly provide remarkable accuracy in predicting depression. This survey study is useful in identifying depression accuracy in advanced ways utilizing diverse resources.

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Vaasudev Sharma Riyansh Dagar S. Sharanya Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality.

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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Interactive E-Commerce: Addressing Cart Abandonment through Gamified User Engagement

Interactive E-Commerce: Addressing Cart Abandonment through ...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： Mayank Kumar Sohanda Mihir Gupta A. V. Kalpana Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper addresses the pervasive issue of cart abandonment in online shopping, a phenomenon that significantly impacts e-commerce revenue streams. Through an investigation into the underlying causes, we identify the lack of social engagement as a critical factor contributing to this behavior. Our primary objective is to bridge the gap between item selection and purchase completion by enhancing the user experience and boosting conversion rates. To tackle this challenge, we propose a novel approach by integrating gamification strategies into the online shopping experience. Our methods include implementing real-time user activity indicators, such as visual representations of active shoppers and interactive leaderboards, to foster a sense of community and competition among users. Additionally, we introduce social shopping features, such as cart sharing and real-time feedback systems, to promote user collaboration and interaction. Preliminary data analysis reveals a significant increase in conversion rates following the implementation of these gamification strategies. Users are more inclined to complete purchases within a community-centric and competitive environment. Moreover, the introduction of social elements has enriched the overall shopping experience, as evidenced by user feedback and heightened engagement on the platform. In conclusion, our study demonstrates the effectiveness of integrating gamification elements into online shopping platforms to combat cart abandonment. By creating a more engaging and communal shopping environment, users are not only motivated to complete purchases, but also to enjoy a more fulfilling shopping experience. Our findings highlight the potential of gamification as a powerful tool for enhancing e-commerce platforms and offer valuable insights for future advancements in online retail strategies.

关键词： Visualization Technological innovation Sockets User centered design Collaboration Transforms Real-time systems

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in data Engineering and Intelligent computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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