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Streamlining Information Capture and Retrieval with AI-Enabled systems

Streamlining Information Capture and Retrieval with AI-Enabl...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Gadug Sudhamsu Ankita Agarwal Rekha Dhivrani K.S. Bhanu Jeyalaxmi M Ashwini R. Nawadkar Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation.

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Natural Language Processing to Improve Content Retrieval Performance

Natural Language Processing to Improve Content Retrieval Per...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Varsha D. Jadhav Devendra Kumar Doda Lakshya Swarup Ritesh Kumar Rajesh Pandian Princy Rufina Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Latest studies in herbal Language Processing (NLP) have targeted improving the accuracy of statistics retrieval through leveraging machines better to recognize the use and scope of human language. Employing NLP strategies makes it far more feasible to system massive units of files and better identify the applicable content in them. Content related to a user's query can then be taken care of and ranked based on the similarity of the language used in the file. NLP strategies permit a more accurate representation of content material and can help retrieve the maximum relevant statistics. Furthermore, NLP can be prolonged beyond traditional search engines like Google to research textual content-primarily based information, such as emails and social media posts, for areas that include sentiment analysis and customer support. This paper explores the capacity of NLP to enhance content material retrieval overall performance in more than a few situations. It additionally outlines valuable techniques for evaluating the effectiveness of such structures. Dialogue and conclusion refer to approaches by which the implementation of NLP can convey improved accuracy to content material retrieval.

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Efficient Approach Towards detection and Classification for Image Forgery by Using Machine Learning Technique

Efficient Approach Towards Detection and Classification for ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Abbas Hameed Abdul Hussein R. Kavitha Bura Vijay Kumar Ali Ashoor Issa Kassem AL-Attabi College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of ECE Vivekanandha College of Engineering for Women (Autonomous) Tiruchengode India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic university Najaf Iraq

Copy-move forgery (CMF) is one of the most malicious forgery attacks often manipulated to spread misinformation and influence security. In the paper, utilized Machine Learning techniques specifically, a Multiclass Support Vector Machine (MSVM)-based Particle Swarm Optimization (PSO) algorithm was utilized for image forgery. PSO is an optimization algorithm in which a group of particles representing individual solutions moves through a search space to find the best solution. The objective function that the PSO algorithm seeks to minimize or maximize is typically related to the MSVM's performance on a validation dataset. The MICC-F200 dataset was used to verify the uniqueness of CMF and evaluate the model's image classification performance. It's important to note that MSVM based PSO can become computationally expensive and memory-intensive when dealing with a large number of classes. They are also sensitive to noisy or mislabeled data points, which can potentially lead to suboptimal performance. The results obtained from this study reveal that the MSVM-based PSO model achieves a high accuracy of 0.9936 on the MICC-F200 dataset, outperforming other existing models such as VGG16 and Mobile Net, thereby ensuring accurate image classification.

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Investigating Low & High Transfers Training for Multi-task Face Identification Using a Semi-supervised Neuronal Networks

Investigating Low & High Transfers Training for Multi-task F...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Durga Prasad Kavadi Asif Hasan Rajesh Tanna Rajesh Singh Jayapal Lande Mohit Tiwari Department of CSE IcfaiTech (Faculty of Science and Technology) IFHE Hyderabad India Department of Psychology Aligarh Muslim University Aligarh Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology Duvvada Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

Recently years have seen a significant increase in research on visual identification issues including identification, aging, sexuality, & expression identification. This same classification of the majority of non-verbal Living beings Interactions scenarios became dependent on their implementation in robotics systems. Deep convolutional neuronal systems have demonstrated to be efficient in this respect when analyzing various face expressions, but at a heavy price: they need a sizable quantity of task-specific labelled information to attain maximal generalisation. A uniform semi-supervised deep network system is suggested in this research to deal with this issue. Our combination model consists of a set of permutation networks that fine-tunes the high-level faces notions for the identification of individuality, time of life team, sex, & facial expressions, as well as an unsupervised deep generative confrontational connection that gets to know basic character traits of face *** the way you look. To avoid the need for a significant quantity of highly labelled learning information, our system uses continuous laterally interconnections among the convolutional streams so that it can exchange the high-abstraction specifics of all of such jobs. We suggest a number of tests to assess each component of our hybrids system, particularly the influence of the progressing interconnections on acquiring the individual image identification problems, & we find that our framework outperforms task-specific systems.

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Current Status and Prospects of Risk Management Research in the Era of Big Data intelligence 23

Current Status and Prospects of Risk Management Research in ...

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Proceedings of the 2023 3rd international conference on Big Data, Artificial intelligence and Risk Management

作者： Renlong Deng Jiang Jiang Chaomin Ou Zhiyu Chen Huiming Li College of System Engineering National University of Defense Technology China

ISBN: (纸本)9798400716478

This study investigates the current status and assesses the prognostications pertaining to the implementation of risk management within the realm of big data intelligence. Research focal points within this domain are scrutinized utilizing bibliometric analysis. The prevailing innovative aspects of risk management during the era of big data intelligence are delineated, and bespoke innovative strategies germane to distinct domains are systematically examined to address the specific exigencies of each field. Common challenges encountered in the domain of big data risk management are deliberated upon, with a comparative analysis of distinct challenges prevalent in diverse fields. A predictive model is formulated to prognosticate the developmental trajectory of literature output in this field over the ensuing two years. Furthermore, prospective avenues for research, including the augmentation of data governance, fostering cross-disciplinary collaboration, and the development of intelligent decision support systems, are posited. These propositions furnish theoretical underpinnings and pragmatic references to advance the innovation and application of risk management within the ambit of big data intelligence.

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IoT-Enabled Environmental intelligence: A Smart Monitoring system

IoT-Enabled Environmental Intelligence: A Smart Monitoring S...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Yogesh N. Thakare Aarti M. Karandikar Vicky Butram Ashay Rokade Utkarsha A. Wankhade Shrikant Honade Department of Computer Science Engineering(AIML) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science Engineering (DS) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Electronics & Communication Engineering S. B. Jain Institute of Technology Management and Research Nagpur Department of Electronics & Telecommunication Prof. Ram Meghe Institute of Technology & Research Badnera Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Electronics Engg. CSMSS Chh. Shau College of Engg Aurangabad India

In recent years, global scientific and engineering departments have recognized their duty to protect the environment and public well-being by addressing environmental contamination. This work, propose an IoT-based system for continuous environmental monitoring that rapidly detects issues, allowing for prompt corrective actions. Authorized bodies receive real-time pollution data, enabling timely intervention. This approach safeguards public health and reduces the risk of natural disasters caused by environmental imbalances. Propose innovative solution integrates cutting-edge IoT technology for real-time environmental monitoring, creating an up-to-date environmental information database for informed decision-making. Proactive pollution control methods further enhance public health protection and act as a preventive measure against environmental disasters, advancing sustainable environmental management through science and engineering. The system employs Arduino UNO board (ATMEGA328/P), four distinct sensors: MQ-2 gas sensor, humidity sensor, temperature sensor (DHTII), ultrasonic sensor (HCSR04), and ESP8266 Wi-Fi module, which transmits data to open IoT API service ThingSpeak, where it is analyzed and stored at central office for further action.

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Deep Learning based Real-time Stolen Vehicle detection Model with Improved Precision and Reduced Look Up Time

Deep Learning based Real-time Stolen Vehicle Detection Model...

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Intelligent Technologies (CONIT), international conference on

作者： Shaik Shafi T Pavan Sai Kumar Reddy Rohan Silla Musrath Yasmeen Dept of ECE B V Raju Institute of Technology Medak

In the recent past, the number of vehicles stolen in India has surged substantially. As per the Acko Vehicle Theft Report, in Delhi-National Capital Region (NCR), for every 12 minute a vehicle is stolen and overall Delhi-NCR accounts for more than 56% of all vehicle thefts in the country. Further, almost 3 lakh automobiles were stolen in the national capital during 2011 and 2020. While Bengaluru, India’s second-highest car theft city, accounts for 9% of vehicle theft. There exists plethora of studies for stolen vehicle tracking or detection models. The prior methods employed were time-consuming and inefficient, requiring a lot of human work to go through CCTV video recordings and trace the stolen vehicle. Some models are 96% accurate but are incapable of being employed in real time. Some are inaccurate, yet they are utilized for real-time detection. To overcome this time consuming process, a Deep Learning Based Real Time Stolen Vehicle detection Model is proposed in this paper. Our work aims to reduce the lookup time of the vehicle in CCTV footage besides the several tasks to accomplish, like vehicle and license plate detection, optical character recognition. To achieve this, a deep learning tool known as You Only Look Once (YOLO) v8 algorithm has been used for real-time object recognition in the system. In addition, TensorFlow, an open-source machine learning platform is used for vehicle number plate recognition and for image processing. Using this strategy, we can dramatically shorten the time it takes to find the stolen vehicle. Upon rigorous experiments, results showed that the proposed deep learning based real time framework is viable and can achieve decent detection.

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Optimization of Permanent Magnet Synchronous Motor Speed and Rotor Position detection Based on High Frequency Pulsation Voltage Injection Method

Optimization of Permanent Magnet Synchronous Motor Speed and...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Kejia Hu Xizhong Shen School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

Permanent magnet synchronous motor is an important power device in industrial system. To enhance its performance at idle speed, this paper designs a new rotor observer using the conventional voltage signal injection method as a reference. A generalized second-order integrator with center frequency adaptively varying according to the speed is used to extract the response signal of currents, and a square-wave signal with the same frequency as the injected signal and the extracted signal are multiplied instead of a low-pass filter. The simulation model was then built and tested, and after continuous testing it was found that using the new rotor observer designed reduces the speed convergence time by 0.79 s when the motor is started at no load and by 0.6 s when running a sudden load increase at constant speed. It reduces the position error by 0.29 rad when running a speed switch at no load and reduces the position error by 0.11 rad and the speed error by 0.24 r/min when running a sudden load increase at constant speed. The observer designed in this paper can reduce the speed convergence time and rotor position error, has better dynamic performance, and has certain application value.

关键词： Rotors Voltage Switches Observers Signal processing Permanent magnet motors Synchronous motors

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Drowsiness detection and Prevention Models for the Elderly People: A Promising Design 3rd

Drowsiness Detection and Prevention Models for the Elderly P...

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3rd international conference on Machine intelligence and Smart systems, MISS 2023

作者： Dubey, Shivendra Khalaf, Hameed Hassan Almulla, Ausama A. Hussein, Mustafa Asaad Jawad, Israa Abed Department of AIML Manipal University Rajasthan Jaipur India Department of Medical Laboratories Technology Al-Manara College for Medical Sciences Maysan Iraq Department of Medical Laboratories Technology Al-Hadi University College Baghdad10011 Iraq College of Nursing National University of Science and Technology Dhi Qar Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq

ISBN: (纸本)9783031691140

Internet of Things (IoT) devices are becoming increasingly popular at an exponential rate in the modern day because of the ease with which they can be used to keep tabs on a person’s everyday health status. By allowing devices to communicate with one another over the internet, IoT allows users greater freedom of choice in how they define their healthcare infrastructure. Because it utilises sensors and actuators to monitor people’s health, it is essential that it is flexible enough to work with the limited infrastructure of the Internet of *** cases where an old person has fallen, it can alert emergency personnel using picture processing technology. Elderly people require a great deal of care, but in today’s era of busy careers, working adults often lack the time to devote to that responsibility. Those who have virtual relationships with others now cannot imagine life without the Internet of Things. The application of these methods to the development of more effective scientific advances holds great promise for the resolution of many pressing issues. The Internet of Things not only facilitates communication, but also raises awareness of numerous pressing matters. This paper introduces the Elderly Fall detection system, a monitoring system for detecting falls that uses Internet of Things (IoT) and Internet Protocol (IP) approaches to warn others before a loss occurs. There are many proposed algorithms for determining tiredness, however the majority of these methods focus on analysing the subject’s facial expression or even skin tone variations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Stress and Anxiety detection via Facial Expression Through Deep Learning

Stress and Anxiety Detection via Facial Expression Through D...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Mohit Saraswat Roshan Kumar Jyoti Harbola Divyanshi Kalkhundiya Mandeep Kaur Mayank Kumar Goyal Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India

In today's world, stress and anxiety are major issues, particularly for young adults. It is crucial to deal with it because it is difficult to eradicate. Find out how to eliminate tension and anxiety, even in a few days. Learn techniques for reducing stress and how to manage anxiety in daily life. Model to check the stress level is based on the contraction of eyebrows and their deviation from the mean position are used to gauge the level of stress which is then written between 1 to 100, this is done after calculating the dist. Between left and right eyebrow. To gauge one's degree of tension and anxiety, a camera can be used to monitor facial expressions and eye brow movements. The body and emotions revolve around the face. It is possible to learn and evaluate facial features using deep learning models, which is a type of machine learning. We were able to develop a facial recognition system separate and recognise the emotions of human subjects thanks to one of our most recent initiatives. A face can identify if someone is smiling, furious, depressed, or worried, but it cannot distinguish between a smile and a frown.

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