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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Das, Arnab Debbarama, Mrinal Kanti Barik, Rabindra K. National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350399387

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

关键词： COVID-19

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Contribution Unveiling Cutting-Edge Machine Learning Techniques for Image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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Quantum Inspired Zeroshot Classification For Adverse Drug Reactions Detection From Social Media Reviews

Quantum Inspired Zeroshot Classification For Adverse Drug Re...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Dey, Arijit Shrivastava, Jitendra Nath B. P. Poddar Institute of Management and Technology Dept. of Computer Applications WB Kolkata India Invertis University Dept. of Computer Science and Engineering UP Bareilly India

ISBN: (纸本)9798350379716

A deep learning model enhanced with quantum transformer has been introduced in detection of adverse reactions of drugs within the reviews collected from social media written in English language. This proposed model enhanced the human language understanding for detecting adverse reactions of drug. Quantum transformers are used for position embedding and attention purpose. Variational quantum circuit encoded the human language and identify the relevant components of the input reviews as class feature vectors to develop a relationship network for each drug correspond to the condition and review comments on which probability distribution functions applied to detect ADRs found in a medicinal product or not. The correctness of advanced model architecture built on quantum transformer is quite encouraging, and it performs better than other cutting-edge learning models. Achieved an accuracy of 93% with a loss of 0.0393 with this proposed model. Though it is too early to declare that quantum computing has completely replaced. This proposed quantum enhanced transformer helps to show the direction towards quantum enhanced hybrid model design in research. © 2024 IEEE.

关键词： Distribution functions

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2024年第4期9卷 330-339页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui UniversityHefei 230000China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032China School of Computer science School of Cyber Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210000China

In the realm of the Internet of things(IoT),radio frequency identification(RFID)technology is essential for linking physical objects to digital *** introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity(Wi-Fi)***,this trend faces challenges,notably interference from strong Wi-Fi signals,which impacts RFID-based sensing *** paper proposes the WiFID(Wi-Fi and RFID)algorithm,which enables Wi-Fi devices to sense weak RFID harmonic *** subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic *** validation on the universal software radio peripheral(USRP)platform demonstrates that WiFID successfully detects 90% of RFID harmonics at−30 dBm,with only a minimal 4%decrease in Wi-Fi throughput.

关键词： RFID harmonic wireless sensing Wi-Fi interference avoidence

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PiCi - A Casestudy of WebRTC based Image Editing Software System 10

PiCi - A Casestudy of WebRTC based Image Editing Software Sy...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ayesha Nagar, Himanshu Chhabra, Megha Ravulakollu, Kiran Kumar Goyal, Mayank Kumar School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India

ISBN: (纸本)9789380544472

This study suggests using JavaScript and other web development languages to create a graphical user interface for an image editor. With features like rotation, flipping, boosting brightness and contrast, and other edits, users will be able to edit and enhance their digital photographs using the little application that will be described in this paper. In order to establish contact between two users, WebRTC (Web Real-Time Communication) technology has been incorporated with the picture editor. Without using any internal or external software, it is one of the best methods for transferring data, speech, and video between two devices. With the aid of WebRTC, this paper will enable several users to communicate and edit the image over a remote desktop connection. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Graphical user interfaces

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Instantaneous Interpretation into Sign Language for the Hearing Impaired 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Because they are unable to convey their thoughts to others who can hear, deaf persons may have a difficult time controlling their emotions and may find that their routines are often interrupted. This is the primary impetus behind the progression of things. These individuals would be helped by this system, and it would make it possible for them to convey their ideas in a manner that is on par with that of persons who do not have any kind of physical impairment. Recent advances in artificial intelligence have made it possible to design a system that is capable of resolving this problem, which was previously thought to be impossible. The purpose of this research is to develop, for the benefit of the deaf community, a system that can translate speech into text. Because it’s possible that some individuals won’t comprehend only the written word, the speech will also be translated into international sign language (ISL). In this method, persons who are deaf or hard of hearing will have their sign language translated into spoken language. In order to develop the most accurate model possible, we will integrate NLP with a variety of ML and AI techniques. For the purpose of prediction, convolutional neural networks (CNN) will be used since lip movements are continuous and rapid, making them challenging to capture. CNN and attention-based long and short-term memory (LSTM) approaches will function since they are both effective at predicting visual input. In order to get more desirable results, we will be using several techniques for augmenting the data. TensorFlow and Keras are going to be the Python libraries that are employed in order to do the voice-to-text translation. At the moment, there is an abundance of software accessible;however, all of them require the use of a network. This piece of hardware can function very well without being connected to the internet. Using the suggested technique, we were able to attain a 100% accuracy rate in sign language prediction and a 96% a

关键词： Forecasting

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain Technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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