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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Savanur, Abhishek Niranjanamurthy, M. Amulya, M.P. Dayananda, P. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore560054 India Department of Computer Science and Engineering Karnataka 571448 India Jss Academy of Technical Education Department of Ise Bengaluru560060 India

ISBN: (纸本)9781665435871

ChatBot can be described as software that can chat with people using artificial intelligence. A chatbot is a PC program that reenacts human discussion through voice orders or text visits or both. Chatbot, short for chatter-bot, is a man-made reasoning (AI) include that can be implanted and utilized through any significant informing applications. This software are recycled to achieve tasks such as rapidly responding to consumers, informing them, assisting to purchase products and providing better service to customers. This research paper showing how a chatbot can be created through demonstration as well as their applications of chatbots in numerous areas such as traveling, banking, health, customer call centers and e-commerce. Additionally, the results of an example chatbbot for online food order service developed in the e-commerce domain is presented using Dialog-flow engine and Facebook Messanger. This research work represents the Chat flow, components of a chatbot, Implementation using Dialogflow. © 2021 IEEE.

关键词： Sales

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Fingerprint definition for song recognition using Machine Learning Algorithm

Fingerprint definition for song recognition using Machine Le...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Arunakumari B N Shashidhar R Sahana B Jagadamba G A S Manjunath Roopa M Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Dept. of Electronics & Communication Engineering JSS Science and Technology University Mysuru India Dept. of Electronics & Communication Engineering RV College of Engineering Bengaluru India Dept. of Information Science and Engineering Siddaganga Institte of Technology Tumkuru India Dept. Of Master of Computer Applications JSS Science and Technology University Mysuru India Dept. of Electronics & Communication Dayananda Sagar College of Engineering Bengaluru India

A condensed content-based signature that summarises an audio recording is known as an audio fingerprint. Audio The capability to recognize music regardless of design and without the use of meta-data or watermark embedding has made fingerprinting technologies popular. In. tegrity checking, watermark support, and content-based aural retrieval are additional applications for fingerprinting. The various methods of fingerprinting have been discussed using various justifications and terminologies, such as pattern matching, multimedia information retrieval, or cryptography. It is a distinct and condensed digest created using perceptually important elements of a video. Audio material can be monitored using fingerprinting technologies without requiring embedding of metadata or watermarks. However, audio fingerprinting has extra applications The algorithm is trained using thousands of mini audio speech commands. In order to improve the training efficiency, audio features are extracted during a pre-processing. The accuracy of the developed model is 83.7%.

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A Surveillance System Focused on Approved Blockchains and Computation of Edges 1

A Surveillance System Focused on Approved Blockchains and Co...

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1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Victor Benevent Raj, A. Srikanth, B. Thilagavathy, A. Mathivanan, B. Department of Master of Computer Applications Ananda College Tamil Nadu Devakottai India Department of Computer Science and Engineering SRK Institute of Technology Krishna District Andhra Pradesh Enikepadu India Department of Computer Science and Engineering R.M.K Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore India

Video surveillance systems have become a critical tool for urban management in recent decades. Without visiting the scene, managers may grasp the scene information. The monitoring system can improve management and supervision effects and reduce the likelihood of major accidents. With both the invention of the Internet of Things (IoT) era, however, sensor networks face difficulties, like massive access to equipment, massive data, inadequate bandwidth, attack vulnerability and real-time surveillance problems. We provide an overview of the implementations situation of the visual supervision system accordance with the project blockchain and border computers. The framework uses BCs, edge processors, developments from the IPFS and transformed neural networks. Edge computing is used for huge wireless communication collection and analysis. The IPFS storage service can be used and CNN infrastructure is used in legitimate surveillance to do a large video collection. © Published under licence by IOP Publishing Ltd.

关键词： Blockchain

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield

An Adaptive Deep Learning Framework for Prediction of Agricu...

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

作者： K Rama Rao Manthena Swapna Kumari Ravindra Eklarker Pundru Chandra Shaker Reddy Kirti Muley Vijay Kumar Burugari Electronics and Communication Engineering Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(AIML &IoT) Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of AI &DS Vardhaman College of Engineering Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Master of Computer Applications Bharati Vidyapeeth's Institute of Management and Information Technology Navi Mumbai India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos.

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Cuckoo Search Optimization Algorithm and Its Solicitations for Real World Applications

Cuckoo Search Optimization Algorithm and Its Solicitations f...

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作者： Niranjanamurthy, M. Amulya, M.P. Niveditha, N.M. Chahar, Dharmendra Department of Master of Computer Applications M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering B G S Institute of Technology Adhichunchanagiri University B G Nagar Mandya571448 India M D College Pallu Rajasthan Hanumangarh India

Cuckoo Search is a streamlining calculation created by Yang and Deb in 2009. It is utilized to take care of advancement issues. It was enlivened by a types of winged animal considered a cuckoo that lays its eggs in the homes of other host fowls. The laying and proliferation of cuckoo eggs is the principal essential inspiration for the improvement of another enhancement calculation. This advancement calculation builds proficiency, precision, and union rate. The cuckoo streamlining calculation (COA) is utilized for persistent non-direct enhancement. The calculation can be reached out to more perplexing situations where each home has a few eggs that speak to a lot of arrangements. This exploration work speaks to a concise survey of the cuckoo search calculation just as streamlining and its issues. Diverse cuckoo search classes and different cuckoo search applications are inspected. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optimization

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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The Performance Investigation of Smart Diagnosis for Bearings Using Mixed Chaotic Features with Fractional Order

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Sensors 2023年第8期23卷 3801-3801页

作者： Li, Shih-Yu Tam, Lap-Mou Wu, Shih-Ping Tsai, Wei-Lin Hu, Chia-Wen Cheng, Li-Yang Xu, Yu-Xuan Cheng, Shyi-Chyi Graduate Institute of Manufacturing Technology National Taipei University of Technology Taipei10608 Taiwan Institute for the Development and Quality 999078 China Department of Electromechanical Engineering Faculty of Science and Technology University of Macau 999078 China Master Program Graduate Institute of Mechatronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung City202301 Taiwan

This article presents a performance investigation of a fault detection approach for bearings using different chaotic features with fractional order, where the five different chaotic features and three combinations are clearly described, and the detection achievement is organized. In the architecture of the method, a fractional order chaotic system is first applied to produce a chaotic map of the original vibration signal in the chaotic domain, where small changes in the signal with different bearing statuses might be present;then, a 3D feature map can be obtained. Second, five different features, combination methods, and corresponding extraction functions are introduced. In the third action, the correlation functions of extension theory used to construct the classical domain and joint fields are applied to further define the ranges belonging to different bearing statuses. Finally, testing data are fed into the detection system to verify the performance. The experimental results show that the proposed different chaotic features perform well in the detection of bearings with 7 and 21 mil diameters, and an average accuracy rate of 94.4% was achieved in all cases. © 2023 by the authors.

关键词： Fault detection

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Safe Transportation System using IoT based Alcohol Detection 7

Safe Transportation System using IoT based Alcohol Detection

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Pradeepkumar, G. Vijayakumar, P. Chandrasekaran, N. Rohith Bhat, C. Senthilkumar, C. Kumar, Neelam Sanjeev KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering SIM ATS Chennai India SIMATS University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Biomedical Engineering SIMATS Chennai India

ISBN: (纸本)9798350397253

Every year, drunk driving injures and sometimes kills a number of people. It is critical to investigate an IoT-based alcohol detection system for safe mobility. Because it uses sensors and a microprocessor to monitor the driver's BAC and cuts off the car's ignition if it suspects that the driver has been drinking while driving, this system can detect and prevent drunk driving. The proposed system will use IoT to remotely monitor and analyze the driver's Blood Alcohol Level (BAC). A communication module, a microprocessor, and an alcohol sensor comprise the system. The communication component sends real-time data to a BAC server for monitoring and analysis. The proposed strategy reduces the likelihood of accidents, improves driver and passenger safety, and lowers the likelihood of transportation companies having to pay for injuries or damages. IoT-based technologies that detect alcohol can aid in the prevention of drunk driving. By monitoring drivers' BACs in real time and discouraging drunk driving, this device has the potential to significantly reduce traffic-related injuries and fatalities. © 2023 IEEE.

关键词： Internet of things

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Design of Adaptive Priority Based IoT Communication in Wireless Network

Design of Adaptive Priority Based IoT Communication in Wirel...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Prabha, R. Senthil, A.G. Saranya, N. Naga Arun, M. Somasundaram, K. Karthikeyan, C. Sri SaiRam Institute of Technology Dept of Electronics and Communication Engineering Chennai India Dept of Electronics and Communication Engineering Chennai India Meenakshi College of Engineering Dept of Master of Computer Application Chennai India Panimalar Institute of Technology Dept of Electronics and Communication Engineering Chennai India SIMATS Saveetha School of Engineering Dept of Institute of Information Technology Chennai India Koneru Lakshmaiah Education Foundation Department of Comuter Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665495158

Heterogeneous wireless networks of the future must be able to satisfy ever increasing demands while also making the best use of available resources. Relaying IoT contact has emerged as a novel promising approach for improving coverage, lowering power consumption, and reducing the possibility of outages. Enabling cooperation among nodes significantly increases throughput in a multi-hop heterogeneous wireless system to provide optimal IoT communication. In recent IoT communication developments, relay selection has a significant impact on network performance. IoT communication, particularly in surveillance-based wireless networks, has a number of advantages over traditional direct communication, and relay selection is widely regarded as a smart way to increase capacity efficiency. The observation of numerous relying approaches prompted the creation of a better way to solve the imprecise nature of wireless networks by using adaptive priority-based relay selection decisions. In this work, a multi-hop terminal scenario, IoT communication to develop an efficient method for selecting a better relay in each IoT device, as well as to investigate the various user options for relay selection parameters. The results is experiments using NS2 (Network simulator) and implementation of Throughput, Packet Delivery Ratio (PDR) and End-to-End Delay performance are tested. © 2022 IEEE.

关键词： Quality of service

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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