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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Real-Time Insights and Dynamic Performance Visualizations for Unprecedented Impact in Ball Badminton 3rd

Real-Time Insights and Dynamic Performance Visualizations fo...

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3rd International Conference on Sports engineering, ICSE 2023

作者： Rohith Gowda, M. Pai, Aditi N. Vishwanatha, C.N. Akash, M.P. Ranganath, G.H. Bhaskar, H.N. Department of Computer Science and Engineering R. V. College of Engineering Bengaluru India Department of Physical Education and Sports R. V. College of Engineering Bengaluru India Department of Physical Education and Sports SJB Institute of Technology Bengaluru India Department of Physical Education and Sports SBRRMFGC Mysuru India

ISBN: (纸本)9789819782963

The absence of advanced analytical tools has been a major obstacle in the world of ball badminton, a traditional sport. The lack of real-time data and valuable insights has long impeded players, coaches, and spectators. This paper introduces an innovative system aimed at transforming the ball badminton experience, enhancing player performance, engaging spectators, and providing valuable tools for coaches and fans through real-time insights and dynamic performance visualizations. This approach involves developing a system, preferably an application, to track the movement of the player and measure position, acceleration, and speed using computer vision and predicting tactics to assist during training, tracking the short trajectory of the ball to assist during practices for improvement, displaying live statistics such as player stats (points won, errors, aces), match progress (scoreboard), and historical data (head-to-head records) to provide context and analysis during the match, besides assisting the umpire in making decisions. Benefits to the viewers are the display of real-time statistics, fan engagement through quizzes on the sport, and result prediction. Coaches can use it to make strategic decisions, players to review their performance, and fans to gain deeper insights into the match. Additionally, it employs a markerless motion capture system, which relies on deep learning for human position estimation and computer vision techniques. To transform pixel data into court coordinates and enable the measurement of parameters like distance covered, court positioning, and average player speeds for ball badminton players, the methodology relies on the inverse perspective mapping method. One of the algorithms applied in this context is the Lucas-Kanade optical flow algorithm, which aids in player tracking and position estimation, contributing to the overall effectiveness of the technology. The technology will track player movements and tactical insights with great preci

关键词： Deep learning

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily *** paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert *** proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,*** proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and *** key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and *** proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and *** comparing with existing technologies,the proposed algorithm gave a better error rate and less *** rate and time is decreased by 19.79%,5.13 *** the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT *** proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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RECRIoT-MAC: A Reliable and Energy Efficient Resource Management for Internet of Things Based Cognitive Radio Multi- Channel MAC Protocol

RECRIoT-MAC: A Reliable and Energy Efficient Resource Manage...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Currently, the overcrowding of unlicensed spectrum bands is causing severe interference to IoT devices embedded with sensors. CR-IoT networks apply cognitive radio capabilities to the internet of things to solve the spectrum scarcity issue in conventional IoT networks. An CR radio detects available spectrum channels by dynamically modifying its programming and configuration. IoT, smart cities, and 5G all benefit from cognitive radio applications. A cognitive radio application is useful for IoT, smart cities, and 5G. Compared to benchmark Cognitive Radio MAC (CR- MAC) protocols, the proposed protocol is more reliable, intelligent, and smart. CR technology for IoT applications can be advanced by us in order to make IoT more affordable and applicable. It is demonstrated by comparing the proposed RECRIoT-MAC protocol with benchmark CR-MAC protocols that is more energy efficient and consumes less communication time. This paper examines RECRIoT-MAC's energy consumption and throughput. © 2024 IEEE.

关键词： Cognitive radio

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Optimized convolutional neural network–multilayer perceptron for the detection of COVID-alike viruses using genome sequencing data

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European Physical Journal Plus 2025年第5期140卷 1-16页

作者： Prabha, Bindhu Divya, S.V. Department of Computer Application Noorul Islam Centre for Higher Education Tamil Nadu Kanyakumari India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Anna University Tamil Nadu Coimbatore India

The COVID-19 pandemic caused by extremely serious respiratory syndrome SARS-Cov-2 has been spreading fatally at an exceptional pace. It has spread to millions of individuals and persists to significantly affect the health and well-being of the global population. Under this circumstance, researchers and medical experts may find it easier to comprehend the genetic variations of SARS-Cov-2 or COVID-19 with the use of advanced artificial intelligence algorithms and genome sequence analysis. Genome sequence evaluation of COVID-19 is important to comprehend the structure, origin, and behaviors of virus that may assist developing/producing antiviral drugs, vaccines, and proficient preventive schemes. This research develops a new optimization approach, called Trend Factor Smoothing Lyrebird Optimization Algorithm (TFSLOA) that is an integration of Trend Factor Smoothing (TFS) and Lyrebird Optimization Algorithm (LOA), to train learning rate of 1D CNN-MLP model for viruses, like COVID-19, SARS-Cov-2, MERS, and Orthomyxo analysis. Initially, the input genome sequence is fed to feature extraction that is performed by entropy, encoded genes, and n-gram features. Subsequently, genome sequencing data analysis is performed by employing 1D CNN-MLP which is adopted by a combination of 1D CNN and MLP techniques. Moreover, 1D CNN-MLP is trained by using TFSLOA. Finally, the experimentation analysis reveals that TFSLOA attains better accuracy of 0.947, precision of 0.951, recall of 0.953, and F-measure of 0.953. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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Phishing Websites Classification using Extreme Learning Machine

Phishing Websites Classification using Extreme Learning Mach...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Gowroju, Swathi Choudhary, Shilpa Jyothi, G. Sabitha, B. Kumar, B. Bikram Srilakshmi, R. Hyderabad India Hyderabad India Hyderabad India Cvr College Of Engineering Department Of Computer Science & Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department Of Computer Science & Engineering Vaddeswaram India Department Hyderabad India

ISBN: (纸本)9798350366846

Phishing presents a widespread and perilous menace within the domain of cybercrime, functioning as a rapid and uncomplicated means for cybercriminals to get confidential personal data. Phishing attacks specifically target unsuspecting persons with the intention of obtaining sensitive information, such as usernames, passwords, and financial credentials. Phishing websites frequently incorporate indicators inside their textual content and browser cookies. In order to address this issue, the suggested method makes use of the UCI Phishing Websites Data and implements categorization based on Extreme Learning Machine (ELM). In contrast to gradient-based algorithms, the ELM approach employs a solitary hidden layer of neurons that include input weights produced randomly. This design facilitates effective feature learning from data with a high number of dimensions, eliminating the need for iterative changes. The evaluation yielded a remarkable accuracy of 95.34% when compared to other machine learning techniques such as Random Forest Classification and Logistic Regression. © 2024 IEEE.

关键词： Iterative methods

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Development of a Novel Quality of Service Aware Video Streaming on Demand System for the Internet of Thing Enabled MANET

Development of a Novel Quality of Service Aware Video Stream...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

ISBN: (纸本)9798350366846

A mobile ad hoc network (MANET) uses mobile devices and wireless networking to stream video. Several methods have been developed to address the challenges associated with video streaming over MANETs. This paper designed a new protocol for Mobile Video of Demand (MVOD) system architecture over the MANET environment. The proposed MVOD Model is used to improve the QoS parameters of the video system in wireless ad-hoc networks. The MVOD protocol uses client caching algorithms and a staggered broadcasting method, and it compares its performance with that of an existing traditional protocol for three-tier architecture. When compared to QoS parameters such as service delay and bandwidth utilization, the proposed MVOD model performs better than the traditional approach when compared with various scenarios. Based on the importance and complexity of evaluating the techniques, we conclude that comparability problems exist in research. © 2024 IEEE.

关键词： Quality of service

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Diabetes Mellitus Diagnosis using Optical Ring Resonators 18

Diabetes Mellitus Diagnosis using Optical Ring Resonators

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajwal, Manush Jesy Janet Kumari, J. Uppu, Maanas Mitrahass Chirchi, Vanajaroselin Vishalatchi, S. Darshan, D.N. The Oxford College of Engineering Dept. Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Isc Bangalore India Kalasalingam Academy of Research and Education Dept. of Ece Krishnankoil India

ISBN: (纸本)9789380544519

The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach's ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： diabetes diagnosis diabetes mellitus non-invasive method optical signal ring resonator

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