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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 IEEE.

关键词： Traffic congestion

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An Optimized Combinatorial Approach For Stock Market Prediction Considering User Preferences 1

An Optimized Combinatorial Approach For Stock Market Predict...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Iyappan, P. Anuradha, K. Revathi, R. Sathish Kumar, L. Saravanan, D. Maragatharajan, M. Vellore Institute of Technology Tamil Nadu Vellore India Karpagam College of Engineering School of Computer Applications Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kotrikalan India

ISBN: (纸本)9798350372748

The economy is often seen as the engine that propels a nation forward. It regulates the distribution of resources by keeping an eye on the complex relationship between production and consumption. To put it simply, the economic system is there to meet the demands of all the people and businesses that make up the economy. The stock market is an important part of any economic understanding since it is the setting in which activities such as the purchase and sale of shares and the issue of new shares by different types of limited liability companies (both public and private) take place in relation to market research and exchanges. Because it is so difficult for the average person to predict when stock prices will go up or down, it is critical to find the best stocks on the market and put their money into them. When it comes to intelligently and reliably meeting user demands, however, conventional processes fall short. An optimized stock prediction system that takes user preferences and demands into account is presented in this study as a new way to manage retrieval. This technique is crucial for properly satisfying user requirements. Essential aspects such as supply-demand dynamics, inflation, geopolitical events, technological advancements, natural catastrophes, company performance, and the efficiency of government data may be dynamically gathered using this technique. All of these things add together to make stock price swing predictions. Machine learning algorithms are crucial for stock prediction because of the massive amounts of data that must be collected, processed, cleaned, and analyzed. The proposed method, ML + LSTM, yields 96% accuracy. Comparing the existing methods, which are KNN, decision tree, and random forest, gives accuracy like 80, 92, and 94 percentages. In addition, the article highlights the use of ML techniques including k-nearest neighbour, decision tree, and random forest. Important financial indicators, including common factors, sector factors,

关键词： Decision trees

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Prediction of Traffic Accidents Using Deep Learning Ensemble Model

Prediction of Traffic Accidents Using Deep Learning Ensemble...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sekhar, G Soma Mahadik, Shubhangi Ramana, Attili Venkata Chandra Shaker Reddy, Pundru Pughazendi, N. Asif, S. Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Bharati Vidyapeeth's Institute of Management & Information Technology Master of Computer Applications Navi Mumbai India Hyderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management CSE-Data Science Telangana Hyderabad India

ISBN: (纸本)9798350319200

Because of the widespread negative effects that traffic accidents can have on people's lives, the study of how to assess and anticipate them using cutting-edge technology is an important area of study. The research of traffic accidents has traditionally relied on data made public by traffic-entities and road-police forces. However, with the advent of social-media, researchers now have access to detailed, real-time data on road accidents occurring across a region, allowing for more comprehensive analyses that take into account previously unreported incidents. This paper uses an ensemble-Deep-Learning(EDL) strategy made up of Gated-Recurrent Units(GRUs) and Convolutional-Neural-Networks(CNNs) to offer a model to forecast traffic accidents utilizing data mined from social-media and the open web. The acquired results are compared to those of published baseline methods and other researchers. The outcomes are encouraging, showing that the proposed EDL approach outperforms the conventional models and other DL approaches published in the literature when applied to the problem setting. Authorities in charge of preventing accidents on the roads can use the data generated by the program. © 2023 IEEE.

关键词： DL ML risk prediction traffic accident traffic accidents

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Optimization-Based Collision Avoidance in Underwater Wireless Sensor Network 1

Optimization-Based Collision Avoidance in Underwater Wireles...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sugumaran, S. Buvaneswari, B. Senthilkumar, K.S. Jeevitha, R. Ramachandra, A.C. Vishnu Institute of Technology ECE Department Bhimavaram India Panimalar Engineering College Department of Information Technology Chennai India Sri Ramakrishna College of Arts & Science Department of Computer Applications Coimbatore India KPR Institute of Engineering and Technology Computer Science and Engineering Arasur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The Underwater Wireless Sensor Network (UWSN) is crucial for private, military, and commercial maritime operations, like environmental monitoring, underwater research, scientific data collection, and underwater wireless communications. The existing protocols for Terrestrial Wireless Sensor Networks (TWSNs) could be better in terms of power efficiency, reliability, and effectiveness. Due to their unique characteristics. The energy-efficient convergent depth variation routing (ECDVR) algorithm is introduced to evaluate and reduce power consumption by calculating the distance between each node point and node depth variation. This algorithm considers the two-hop neighbour depth variation when sending data to a neighbor node with a higher forward angle toward the receiver. It calculates the receive time contribution of the Adaptive Time Difference of Arrival (ATDoA). Adaptive Time Difference of Arrival (ATDoA) The arrival time of a signal makes it simple to estimate the time difference between two signals at a node. The storage node-to-receiver approach is the one that has been proposed for determining the node's remaining power for each information exchange. In the subsequent UWSN transmission, the sink allocates the node's higher power when the node reduces its power. Extending the packet's lifetime and shortening the time it takes to reach its destination boosts system performance. It outperforms the compared algorithms regarding node lifetime, dead node count, power consumption, and propagation delay. © 2023 IEEE.

关键词： Energy efficiency

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Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

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computer Systems science & engineering 2022年第11期43卷 439-454页

作者： Ravindra Parshuram Bachate Ashok Sharma Amar Singh Ayman AAly Abdulaziz HAlghtani Dac-Nhuong Le School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India Department of Computer Science and IT University of JammuJammu and Kashmir180006India School of Computer Applications Lovely Professional UniversityPunjab144001India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia School of Computer Science Duy Tan UniversityDanang550000Vietnam Institute of Research and Development Duy Tan UniversityDanang550000Vietnam

Communication is a significant part of being human and living in the *** kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different *** application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models ***,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional *** is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and *** paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median *** preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech *** optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

关键词： Deep learning grasshopper optimization algorithm recurrent neural network speech recognition word error rate

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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Password Strength Analysis and its Classification by Applying Machine Learning Based Techniques 2

Password Strength Analysis and its Classification by Applyin...

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2nd International Conference on computer science, engineering and applications, ICCSEA 2022

作者： Sarkar, Sakya Nandan, Mauparna Asansol Institute of Engineering & Management-Polytechnic Department of Computer Science & Technology Asansol India Haldia Institute of Technology Department of Computer Applications Haldia India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Passwords serve as a vital authentication mechanism for any system's protection. Although numerous more secure methods of authentication exists such as biometrics and smart cards but still the largely popular technique is password authentication for ensuring protection of any system on account of its ease of implementation. But, passwords are exposed to various types of vulnerabilities due to the predictable patterns which human employs for setting passwords likely dictionary words, commonly used people and places names, keyboards patterns, date of birth, known phrases etc. and thereby, pave a route for online or offline muggers to intrude into the system by assuming the passwords. For guessing the passwords, various password cracking tools are available either online or offline and most of the tools can readily crack such accounts possessing passwords with weak strengths or passwords with common patterns. Therefore, the organizations should enforce unassailable strategies governing the use of strong passwords such that those vulnerabilities can be addressed. Hence, the most efficient way to protect the system against these online and offline attacks is by enforcing the users to implement a strong password. In this proposed work, prediction of the strength of a password has been modeled as a classification task with the aid of multiple supervised machine learning algorithms which were used for the learning purpose. The novelty of this work lies in the fact, that two new algorithms namely, XGBoost and Multilayer Perceptron have been implemented to determine the strength and the corresponding category of a password which has not been evaluated in similar other works. However, during testing the models with the test dataset, it has been observed that XGBoost outperformed the other machine learning classifiers with an accuracy of 99%. The final results reveal that machine learning approaches possesses ample capability to classify the passwords into different categories,

关键词： Smart cards

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Augmenting Content Retrieval Using NLP in AIML 4

Augmenting Content Retrieval Using NLP in AIML

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shrivastava, Kapil Quezada, Angeles Sharma, Karuna Marquez Lobato, Bogart Yail Vashishth, Nitish Gla University Department of Computer Engineering & Applications UP Mathura India Tijuana Baja California México Computer Science & Engineering Sdgi Global University Tecnológico Nacional de México Campus UP Ghaziabad India Computer Science & Engineering Sdgi Global University UP Ghaziabad India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India

ISBN: (纸本)9798350364729

The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs. © 2024 IEEE.

关键词： Knowledge representation

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Map Reduce Techniques for Machine Learning in Big Data Analytics 7

Map Reduce Techniques for Machine Learning in Big Data Analy...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Jawahry, Hassan M. Shrivastava, Anurag Bansal, Saloni Raju, Hemanth Begum, Rowsonara Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. The problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. This research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. Through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries. © 2024 IEEE.

关键词： MapReduce

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