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Design of Human Resource Allocation Algorithm Based on Convolutional Neural Network

Design of Human Resource Allocation Algorithm Based on Convo...

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Networking, Communications and Information Technology (NetCIT), international conference on

作者： Kang Du Jiangxi university of Applied Science Nanchang China

With the wide and in-depth application of new generation information technologies such as big data and mobile Internet, the Internet has become the main means for people to obtain information, which makes the amount of information available to people surge. Accordingly, people gradually improve their efficient and personalized needs for information acquisition. Recommendation services just meet the requirements of users in these two dimensions. Recommendation tasks often face sequence information. Sequence recommendation is suitable for this scenario. The interaction between users and items is modeled as a dynamic sequence, and the time sequence of the sequence is used to capture users' preferences in the recent period of time. The computing power of hardware and the theory of neural network are also developing and improving. The data-driven deep learning method is sweeping in. Combining it with human resource allocation algorithm has gradually become a research hotspot, especially the application of convolutional neural network in sequence recommendation. Taking convolution neural network as the technical background, through the discussion of the optimal allocation of human resources, and based on the methods of system analysis and quantitative evaluation, this paper establishes the optimal allocation model of human resources, which provides a specific method of quantitative management for the optimal allocation of human resources.

关键词： Training Neural networks Internet Information and communication technology Resource management Convolutional neural networks Remuneration

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Artificial Intelligence Application for Effective Customer Relationship Management

Artificial Intelligence Application for Effective Customer R...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Somanchi Hari Krishna N. Vijayanand Achanta Suneetha S Mahabub Basha S. Chandra Sekhar A. Saranya Vignana Bharathi Institute of Technology Hyderabad India Vels Institute of Science Technology and Advanced Studies Chennai India Sree Vidyanikethan Institute of Management Andhra Pradesh India International Institute of Business Studies Bangalore India

Relationship management with customers (CRM) systems are widely used by businesses to foster a customer-centric culture. With the help managing relationships with clients (CRM) systems and technologies like with the use of artificial intelligence (AI) and the internet of things (IoT), companies may keep tabs on their customers at all times, monitor their whereabouts, and compile information about their purchases, product usage, and loyalty churn. In order to automate customer relationship management (CRM), businesses have begun using artificial intelligence to respond to consumers' inquiries and the resources they require to answer inquiries from and retain more of those customers. The company's leadership is consistently prepared to win new customers, satisfy existing ones, keep them around, and increase their lifetime value. This facilitates a more amicable interaction between consumers and top management by catering to their wants and needs. Strong client loyalty is the engine that powers long-term success and expansion. Management places a premium on building a team that can bring in new business, satisfy existing clients' expectations, and offer timely feedback to the upper echelons of the company. Furthermore, we used multiple correspondence analysis and clustering to create the conceptual structure map related to Customer Relationship Management and data Mining-based CRM. The investigation uncovered an intriguing trend toward CRM research's future reliance on machine learning and AI methods. Research into customer relationship management We outline the current state of customer relationship management (CRM) and data mining-based CRM, and we call attention to promising new directions for future research.

关键词： Industries Customer relationship management Companies Reliability theory Software Software reliability Internet of Things

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Multi-omics data Integration Model based on Isomap and Convolutional Neural Network

Multi-omics Data Integration Model based on Isomap and Convo...

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international conference on Machine Learning and Applications (ICMLA)

作者： Abedalrhman Alkhateeb Bashier ElKarami Hazem Qattous Abdullah Al-Refai Noor AlAfeshat Behnam Shahrrava Mohammad Azzeh Software Engineering Department Princess Sumaya University for Technology Amman Jordan Department of Electrical and Computer Engineering University of Windsor Windsor Canada Data Science Department Princess Sumaya for Technology Amman Jordan

The recent advances in genetics technologies have led to high-throughput characterized different biological molecules’ functionalities. The availability of heterogeneous omics sparked the challenge of integrating them for further analysis. This work incorporates the Isomap technique to embed multi-omic data into a convolutional neural network (CNN). The deep learning model fuses three omics data, which are gene expression, copy number alteration (CNA), and DNA methylation data, for breast cancer stage prediction. Isomap is utilized to convert the high-dimensional data into 2-dimensional maps. The gene similarity network (GSN) map is created based on gene expression data to preserve gene relationships. The values from three omics for each sample are used to color the GSN map based on the RGB system. The created GSN maps for all samples are fed into the CNN for *** model was applied to TCGA breast invasive carcinoma data set to predict the stage of breast cancer. It outperformed the state-of-art iSOM-GSN model in performance metrics, including accuracy, precision, recall, f1-measure, and area under the curve (AUC). The results indicate that a combination of Isomap embedding technique and CNN can successfully integrate a multi-omics data set for cancer outcome prediction, including the diagnosis and prognosis of the complex disease.

关键词： Measurement Limiting data integration Predictive models Prediction algorithms data models Breast cancer

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Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping

Continuous Human Activity Recognition using Radar Imagery an...

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international conference on Machine Learning and Applications (ICMLA)

作者： Ruchita Mehta Vasile Palade Sara Sharifzadeh Bo Tan Yordanka Karayaneva Centre for Computational Science and Mathematical Modelling Coventry University Coventry UK Department of Computer Science Swansea University Swansea UK Faculty of Technology and Communication Sciences Tampere University Finland School of Computing Engineering & Digital Technologies Teesside University Middlesbrough UK

Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named UnSup-PLevel). In the other two strategies, a Convolutional Variational Autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations.

关键词： Heuristic algorithms Time series analysis Pipelines Radar detection Streaming media Radar imaging Feature extraction

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Discovery and Deterrence of Black Hole Attack in Clustering Ad Hoc Networks Based on Software Agents 3rd

Discovery and Deterrence of Black Hole Attack in Clustering ...

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3rd international conference on computing and Communication, IC3 2020

作者： Aranganathan, A. Suriyakala, C.D. Vedanarayanan, V. School of Electrical and Electronics Sathyabama Institute of Science and Technology Chennai India School of Ocean Engineering & Underwater Technology Kerala University of Fisheries & Ocean Studies KochiKerala India

ISBN: (纸本)9789811573934

Wireless technologies allow mobile devices to activate a wireless ad hoc network powerfully interfacing through the remote medium. A mobile ad hoc network is an assortment of versatile hubs with no defined centralized framework which shares a remote channel. Mobile ad hoc network is uncertain by its tendency, the explanation being the nodes reserve the option to join or leave the system with no limitation whenever at any given time. A node in mobile ad hoc network which experienced some assault displays an abnormal or malicious conduct. A node is said to be a malicious node if and just on the off chance that it experiences with at least one of following qualities: transmission capacity utilization, message altering, session catching, battery depletion, counterfeit directing, postponement of data bundles, connection break, parcel drop, copying data and stale data bundles. Packet dropping attack is called as black hole (BH) attacks is one of the routing attacks in network layer which affects the network performance by sending quick reply to the source node with high destination sequence number. This attack can be avoided using proposed Software Agents based Black Hole attacks Detection and Prevention in Clustering Ad hoc On-Demand Distance Vector (SABHDP-CAODV) routing protocol based on the unique identity of legitimate node, location-based distance calculation, threshold time with the simulation metrics of packet delivery ratio throughput, average end-to-end delay and network routing overhead when compared to existing systems. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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Student Activation in iOER Maker Spaces 21st

Student Activation in iOER Maker Spaces

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21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Kau, Manfred Univ Appl Sci Bonn Rhein Sieg St Augustin Germany

ISBN: (纸本)9783030119324;9783030119317

Interactive Open Educational Resources (iOER) package both information and software to interact with its information in a single component. In Maker Spaces students are following their own ideas, building individual products in small projects, learning about production skills and appropriate machinery. This paper describes concepts and implementation of iOER Maker Spaces based on advanced web technologies, how they contribute to student activation and finally reports on our practical results. iOER Maker Spaces contribute to the societal task of enriching our digital common good.

关键词： OER iOER Maker space WWW Web components Digital common goods Learning analytics Learning data mining

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A Secure and Authentication Scheme to Preserve the Privacy of Electronic Health Records in the Healthcare System

A Secure and Authentication Scheme to Preserve the Privacy o...

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Communication and Information technologies (IICCIT), Iraqi international conference on

作者： Naba M. Hamed Ali A. Yassin Computer Information Systems Computer Science and Information Technology Basrah Iraq Department of Computer Science College for Pure Sciences Basrah Iraq

ISBN: (纸本)9781665472210

Electronic Healthcare Records (EHRs), networks, and cloud computing have significantly revolutionized the traditional healthcare system. Secure remote user authentication is an important component of the healthcare system for authenticating an individual's identification via an unsecure communication network. The roles are distributed among the system's users, contrasting between administrator and data entry within certain rules and policies. It is possible to restrict access to key user activities (addition, deletion, and modification) based on a user's position in the healthcare system. As a result, security issues, hostile attacks, and privacy concerns are viewed as key challenges in the healthcare system. Using role-based access control and smart multi-factor authentication, this research provides a privacy-preserving solution to solve the issues in earlier approaches. The proposed scheme is based on crypto hash functions and asymmetric cryptosystems by using chameleon digital signatures. The proposed scheme carried out two exhaustive formal security proofs for the work that was provided. Using scyther, a formal security tool, we present a secure user authentication technique in the healthcare system that overcomes the familiar security problems and ensures the security of the proposed scheme and informal analysis. Our proposed scheme focuses on two main components: users and administrators. The administrator is considered to be the heart of the system. Therefore, any information that is stolen from them poses a threat to the entire system and all its parts. Compared to others, the proposed scheme more secure. By the way, our work gains many potentials to protect users account gains malicious attacks.

关键词： Heart Privacy Multi-factor authentication Law Memory Medical services Safety

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Plant Disease Detection Using Enhanced EfficientNet Architecture in Comparison with DenseNet to Analyze the Severity in Leaves with Performance Measures

Plant Disease Detection Using Enhanced EfficientNet Architec...

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data science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： T Devi. K. Alice N. Deepa Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of Computing Technologies SRMIST KTR Campus Chengalpattu

ISBN: (纸本)9798350333855

The backbone of the Indian country is enriched with growing crops and cultivation in an effective way. The economical background is a major impact on countries’ financial stages where there is a lack of knowledge to support agriculture as well. In accordance with agriculture and plants development assessment of needs are also analyzed. However, there are several problems to be addressed in crops and plant diseases and the main focus is to reduce the disease on leaves texture that can be associated with the health and wealth of the country. To detect plant diseases there are several methods applied from existing techniques such as Machine Learning (ML) and Deep Learning (DL) where the species produces various results and comparison of results are facing certain limitations. To overcome the limitations in the existing deep learning Convolution Neural Network (CNN) framework such as DenseNet, the Enhanced EfficientNet (EEN) is proposed in the research work to overcome the limitations. To predict the plant disease in the early phase using the proposed algorithm can produce better accuracy such that the quality of crops can be protected without diseases affected. The main step in the proposed idea is to examine the performance by detecting the diseases in leaves by analyzing images from the dataset downloaded from Kaggle in the name of PlantVillage. These datasets were used with the leaf images to detect the severity of plants by extracting multiple features such as textures of multi-labeled leaves, color deviation in the early stage, and shape variations also considered. Classifier using EEN preprocess and classifies accordingly. There is an elimination of regions after segmenting the image according to the classes. Such feature mapping gives the exact results faster with 99.8% of accuracy.

关键词： Deep learning Convolution Shape Plants (biology) Neural networks Crops Feature extraction

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Research on the Welding System of Soft Pack Power Battery Tabs Based on Laser Sensor 6th

Research on the Welding System of Soft Pack Power Battery Ta...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Huang, Jing Lin, Zhe Wang, Zezhou Qi, Yanfeng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Province Industrial Integrated Automation Industry Technology Development Base Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

The soft pack power battery’s lug is prone to welding through and missing welding during the welding process due to its low flatness. A welding track detection system for the soft-pack power battery tabs is designed to solve the soft-pack power battery tabs’ welding difficulties. The system uses a laser sensor to perform non-contact detection on the welding surface of the tab. The system uses a laser sensor to perform non-contact detection on the welding surface of the lug. Then filter the collected data, and perform a non-uniform B-spline curve-fitting on the processed data to generate a welding track curve. Finally, welding the tabs according to the welding track curve can improve the tabs’ welding quality. The experimental results show that the average deviation between the fitted curve and the measured data points is 0.0817 mm, which has a good fitting effect and high calculation accuracy. It can be used on other occasions where this function is needed. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curve fitting

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Intelligent Bandwidth Planner Enhancing Learning technologies in Constrained Distance Learning Environments – a Pandemic Response

Intelligent Bandwidth Planner Enhancing Learning Technologie...

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international conference on Information Technology Based Higher Education and Training, ITHET

作者： Prince Yaw Owusu Amoako Isaac O. Osunmakinde School of Computing College of Science Engineering and Technology University of South Africa Pretoria South Africa Computer Science Department College of Science Engineering and Technology Norfolk State University Norfolk Virginia USA

ISBN: (纸本)9781665489096

Bandwidth resource in open distance electronic learning platform is scarce when services performed by many users contend for bandwidth, causing congestion in the network. The challenge has increased tremendously since almost all academic institutions perform activities online during the pandemic. This paper investigates the inherent workload constraints among open-distance electronic learning (ODeL) services competing for scarce resources and intends to forecast future bandwidth demands to prevent online class disruptions. A predictive framework of bandwidth management, which integrates a sustainable hidden Markov model (HMM) and a normalization policy, coupled with SolarWinds technology for prior network data feeder, is developed. A sustainable HMM $\alpha$ emerges from three HMM candidates based on test priorities on bandwidth demands. Compared to four popular methods, detailed experiments on the proposed model revealed performance analysis of error metrics below 0.5 at peak and off-peak periods. The emerged HMM $\alpha$ reliably predicted bandwidth capacities required to sustain the competing ODeL services with an accuracy of 94%.

关键词： Training Measurement Computer aided instruction Electronic learning Pandemics Hidden Markov models Bandwidth

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