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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Shrikant Kumar, Binod Singh, Rajan Alam, Md. Irfan Kumari, Barkha Hinz, Binita Roshima Mlr Insitute of Technology Department of Electronics and Communication Engineeing Hyderabad India Jharkhand Rai University Facultyt of Cse & It Ranchi India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Jharkhand Rai University Faculty of Cse & It Ranchi India Pune India Rtc Institute of Technology Department of Computer Science and Engineering-Cyber Security Ranchi India

ISBN: (纸本)9798331528201

Mental status through physical appearance identification is not possible as it does not give complete status of any human. Using voice and considering quality extraction of talked it is quite easy to predict the mental behavior and internal variation that will help the doctor related with medical treatment that support them to give proper treatment to person. Hindi is the natural language of India and large community of population talk and speaks this language to exchange and communicate. Various inputs like specific feature, images and vital report of serious person and it is one of the important facts sources for deep training or gaining systems. In this research paper we try to identify the speech signal under different scenario like gun noise, car noise etc. following different feature extraction approaches and compare it for its proper verification. This verification may help to enhance the quality of speech in different adverse scenario and make the speech detection easy. Here, the comparison has been done with various feature extraction method and the results reflects that MFCC has good efficiency of 95% and with a low error rate of 2% compared to other approach. For analysis Hindi voice sample were used for the experimental analysis for 50 samples. © 2024 IEEE.

关键词： Speech enhancement

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Converter design For Self-Power Sensor Of Medical Equipments 8

Converter Design For Self-Power Sensor Of Medical Equipments

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8th International Conference on Electrical, Electronics and Information engineering, ICEEIE 2023

作者： Aripriharta Aprilia, Tiwi Ananda Purwatiningsih, Ayu Yazid, Muhammad Omar, Saodah Binti Hong, Gwo Jiun Department of Electrical Engineering Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering Malang Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Information Technology Surabaya Indonesia Faculty of Engineering Pulau Pinang Malaysia Southren Taiwan University Department of Computer Science and Information Engineering Tainan City Taiwan

ISBN: (纸本)9798350340501

The system for collecting wasted energy and converted into electrical energy is called an energy harvesting system. This paper focuses on the application of a boost converter to harvest leg muscle energy using a permanent magnet generator. A permanent magnet generator is a medium to convert the energy wasted from the leg muscles due to leg movements into electrical energy. The voltage result reaches 1.7Vac with a total 100 steps in 3 minutes. The output voltage generated by the permanent magnet generator is low-voltage with AC current. Therefore, there are stages of designing a rectifier and boost converter circuit in the self-power generation system. The output voltage generated from the rectifier and boost converter circuit is 5 Vdc. The 5Vdc output voltage will be supplied to the supercapacitor for storage. The supercapacitor charging takes ± 1 hour 25 minutes to full. The distance required to fully charging the supercapacitor reaches 5,500 steps on an average of 4.18 Km. Electrical energy stored in supercapacitors can be used by electronic devices such as sensors, IoT, and others. © 2023 IEEE.

关键词： Permanent magnets

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An optimized facial recognition model for identifying criminal activities using deep learning strategy

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International Journal of Information technology (Singapore) 2023年第7期15卷 3907-3921页

作者： Gokulakrishnan, S. Chakrabarti, Prasun Hung, Bui Thanh Shankar, S. Siva Artificial Intelligence Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of Computer Science and Engineering KG Reddy College of Engineering and Technology (Autonomous) Telangana Hyderabad India

The most important research field in the current period is facial recognition and detection. Furthermore, facial expression recognition applications are essential in various research areas such as criminal investigation, security, data base management systems (DBMS), innovative card application, and video surveillance systems. A criminal investigation is an important area of study. Thus, in today’s world, crime activities are fast expanding in response to psychological trauma. As a result, applying deep learning (DL) algorithms for face expression identification and crime activity monitoring is a growing area of investigation. In terms of pre-processing, feature extraction, and recognizing diverse facial emotions, DL produces better results. As a result, the current study seeks to offer a revolutionary intelligent Strawberry-based convolution neural network (SbCNN). When applied to the Kaggle face expression database, the created SbCNN technique performed best. Face verification and criminal face detection can be accomplished using this approach and the optimization fitness function. Even though the established design improves the robustness of feature extraction and classification, it is a time-consuming process. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Data base management system Deep learning Facial recognition and detection Kaggle Optimization Psychological harm Video surveillance systems

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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The Power of PDF-to-Audio Summaries in Modern Learning

The Power of PDF-to-Audio Summaries in Modern Learning

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Dhanshri Kawde Sameer Mendhe Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled.

关键词： Productivity Sentiment analysis Privacy Layout Transforms Portable document format Real-time systems Text to speech Multilingual Robots

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A Large Scale Dataset for Fire Detection and Segmentation in Indoor Spaces

A Large Scale Dataset for Fire Detection and Segmentation in...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Maric, Petar Arlovic, Matej Balen, Josip Vdovjak, Kresimir Damjanovic, Davor Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Fire represents a dangerous event, especially in inhabited areas where it can cause extensive economical damage, as well as take human lives, and therefore early fire detection is of utmost importance and requires careful attention. Utilizing images from security cameras and computer vision algorithms, it is possible to detect and raise the alarm in the event of a fire. The existence of similar-colored lights to the flame's color is the greatest obstacle to indoor fire detection when it comes to computer vision. The lights may trigger false positive detections, resulting in false alarms and potential fire suppression by automated systems. By developing a new fire dataset for the training of deep neural networks, we attempted to circumvent the stated issue. Our dataset includes images of different colored lights, images with reflections of light that resemble the color of fire, and images of fire in a variety of environments, including warehouses, factories, shopping malls, residential buildings, offices, etc. Although there are numerous scientific papers and datasets for fire detection, there are not many datasets containing images of indoor fires. In this paper, we show a process of collecting and annotating images representing indoor fire in a manner suitable for deep neural network training. Furthermore, we present the developed Fire Sense image annotation tool and the process of image annotation. The dataset currently consists of more than 11000 annotated images of various types of fires in different environments. © 2022 IEEE.

关键词： Fires

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Deep Learning-Driven Culvert Monitoring: A Novel Approach for Flash Flood Mitigation through Visual Blockage Analysis 9

Deep Learning-Driven Culvert Monitoring: A Novel Approach fo...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Samuel, Betty Elezebeth Alghamdi, Sultan Priya, T. Deepalakshmi, R. Kannan, Sathish Selvaraju, Mayakannan Department of Computer Science And Information Technology Jazan Saudi Arabia King Abdulaziz University Faculty of Engineering Department of Electrical And Computer Engineering Jeddah Saudi Arabia Npr College of Engineering & Technology Department of Mathematics Dindigul Tamilnadu Natham India The Tamilnadu Dr. Ambedkar Law University Department of Inter-Disciplinary Studies Tamilnadu Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamilnadu Chennai602105 India Vidyaa Vikas College of Engineering And Technology Department of Mechanical Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350394474

The primary cause of urban flash floods is often cited as trash clogging culverts. Flash floods can be avoided with the help of intelligent video analytic (IVA) methods that can extract information about blockages in order to make maintenance-related decisions in a timely manner. Knowing the percentage of culverts that are visually blocked can help prioritise maintenance at heavily blocked locations. In this paper, we introduce a deep learning-powered segmentation-classification pipeline for automatically detecting and segmenting culvert openings and then classifying them into one of four categories based on the degree to which they are visually blocked. The deep learning models underwent training using datasets sourced from the Visual Hydraulics Blockage Dataset (VHD) and Images of Culverts Blockage (ICOB). The outcomes revealed that Mask R-CNN attains the highest performance in segmentation, achieving an mAP@75 score of 77.2%. In contrast, NASNet excelled in classification tasks, with a remarkable 81.2% accuracy on the test data. To underscore the practical importance of these findings, one potential application is demonstrated, which involves using the models for monitoring visual obstructions. © 2024 IEEE.

关键词： computer vision

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Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm

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Journal of computer science & technology 2021年第6期36卷 1388-1406页

作者： Chun-Hui Wang Zhi Jin Wei Zhang Didar Zowghi Hai-Yan Zhao Wen-Pin Jiao Key Laboratory of High Confidence Software Technology(Ministry of Education) Peking University Beijing 100871China Institute of Software School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China School of Computer Science Inner Mongolia Normal UniversityHohhot 010022China Faculty of Engineering and Information Technology University of TechnologySydney 2007Australia

Many applications need to meet diverse requirements of a large-scale distributed user *** challenges the current requirements engineering ***-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user ***,there are still many *** others,a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are *** techniques are needed for supporting the requirements *** are widely used in industry to represent *** paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal *** a result,it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input *** approach is featured by:1)the labelled graph proposed as the representation of the candidate solutions during the iterative evolution;2)the generalized entropy proposed and defined as the measurement of the solutions;3)the genetic algorithm designed for sorting out the high-quality *** cases of different scales are used to evaluate the effectiveness of the *** experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.

关键词： crowd-based requirements engineering requirements synthesis activity diagram genetic algorithm

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer’s Patients

Integrating Decision Trees with Cloud Computing for Predicti...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： K. Meenakshi Bathini Ravinder Mohammed Rehaman Pasha K. Thinakaran Dabbara Jayanayudu S.P. Maniraj Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Siddartha Institute of Science and Technology Puttur Andhra Pradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer’s patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand.

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Change Detection Using Unsupervised Sensitivity Disparity Networks 14

Change Detection Using Unsupervised Sensitivity Disparity Ne...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Yuan, Xiaochen Li, Jinlong Macao Polytechnic University Faculty of Applied Sciences Macao SAR China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

ISBN: (纸本)9798350336313

At present, algebraic operation methods in the field of change detection still holds the dominant position. However, in the face of disturbance features, due to the characteristics of poor expansibility, the performance of algebraic operation methods varies greatly in different scenes, and cannot meet the requirements of practical application. In this paper we propose a change detection model based on Sensitivity Disparity Networks (SDNs) for performing change detection in Bi-temporal Hyper-spectral images captured by AVIRIS sensor and HYPERION sensor over time. The SNDs consist of two deep learning models, Unchanged Sensitivity Networks (USNet) and Changed Sensitivity Networks (CSNet), they have sensitivity disparity in changed and unchanged pixels, and thus to generate effective argument region. Next, we re-evaluate the change probability of argument region, and merge the change result of the argument region with that by one of the SDNs. The detected Binary Change Map (BCM) of the scheme is thus obtained. To train and evaluate the proposed schema we employ two Bi-temporal Hyper-spectral image datasets which contain challenging pseudo-changed features (PCFs) and pseudo-invariant features (PIFs) cause by various external interference factors. The proposed schema outperforms the existing state-of-the-art algorithms on tested datasets. Experimental results show that the proposed schema has good universality and adaptability. © 2022 IEEE.

关键词： Deep neural networks

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