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automation, computing and Renewable Systems (ICACRS), international conference on

作者： Remya Kommadath Aman Kumar Saini Prakash Kotecha Department of Chemical Engineering Indian Institute of Technology Guwahati Guwahati India Department of Physics Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9781665460859

In recent years, the researchers have been studying the potential of Evolutionary Computation (EC) techniques on solving various combinatorial problems. The complex nature of combinatorial problems provides the opportunity for metaheuristic techniques. This work analyzes the efficacy of two winner algorithms of "CEC 2021 special session", a "competition on real parameter bound-constrained numerical optimization" to solve the combinatorial production planning of the petrochemical industry. The optimal production plan specifying the production quantity of each process has to be determined by maximizing the profit while satisfying all the associated constraints. The performance of the winner algorithms, namely Adaptive Gaining-Sharing Knowledge with Improved Multi-Operative Differential Evolution Algorithm (APGSK-IMODE) and Multiple Adaptation Differential Evolution (MadDE), are compared with other popular metaheuristic techniques such as Sanitized Teaching-Learning based Optimization (sTLBO), Dynamic Neighborhood Learning based Particle Swarm Optimizer (DNLPSO), and Artificial Bee Colony (ABC). MadDE algorithm has been employed with a modification that enhanced the algorithm’s performance. It was observed that modified MadDE outperformed all other algorithms whereas APGSK-IMODE determined feasible solutions for all different cases in the problem.

关键词： Industries Renewable energy sources Heuristic algorithms Production planning Metaheuristics Production Petrochemicals

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Two-Stage Deep Learning - YouTube Video Recommendation process

Two-Stage Deep Learning - YouTube Video Recommendation Proce...

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New Frontiers in Communication, automation, Management and Security (ICCAMS), international conference on

作者： Cheepu Balakrishna D. Sharada Mani A Basi Reddy P. Chandrakanth R.Senthamil Selvan Department of Computer Science and Engineering Saispurthi Institute Of Technology Sathupally Mandal India Department of Computer Science and Engineering QIS college of Engineering and Technology Ongole India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Department of Computer Science and Engineering N.B.K.R.I.S.T Vidyanagar Kota India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

YouTube is one of the most widely used and technically advanced commercial systems of recommendations. It provides an overview of the system and highlights the remarkable gains in efficiency made possible by deep learning. This research uses the traditional “two-stage” framework for separating and retrieving information. First, a profound model for generating candidates ought to be described, and then a model for ranking candidates ought to be outlined, and both of these should be done in that order. The order in which these two tasks ought to be completed is important. In addition, it offers instruction that can be put into practice as well as valuable insights that have been gleaned from the process of developing, iterating, and managing a large-scale recommendation system that has a significant impact on the experience of the user.

关键词： Deep learning Training Video on demand Artificial neural networks Watches Web sites Security

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Integration and Validation of a Robust process for Cloud Applications

Integration and Validation of a Robust Process for Cloud App...

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IEEE international conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Sheeba Shahid Sven Eselgrimm Asadullah Shah Ghulam Mahdi Kulliyyah of Computer Science and Information Technology International Islamic University Malaysia ORBIT Gesellschaft für Applikations-und Informationssysteme mbH Germany Kulliyyah of information and Communication Technology International Islamic University Malaysia Kimberly Clark Corporation Roswell GA USA

The development and administration of software applications have undergone a transformation because of the introduction of cloud computing. Flexible, scalable, and cost-effective Software-as-a-Service (SaaS) applications are hosted on scalable cloud infrastructures. However, there are additional difficulties in integrating and evaluating cloud apps, especially given the various control system features of private, public, and hybrid cloud models. The integration and validation process for cloud apps is examined in this research article along with methods for resolving related problems. The paper outlines the identification, connection, data mapping, and testing processes of integration, which is the merging of systems. It makes a distinction between integration between private and public clouds. Cloud-based apps are validated to make sure they adhere to performance, security, and reliability standards. It includes a discussion of testing techniques, including load and stress testing. Lack of control over the underlying infrastructure, changing environments, and shared infrastructure are difficulties in validation. A thorough validation strategy that includes establishing requirements, developing test plans, choosing suitable validation tools, and iterating the validation process is recommended to overcome these challenges. Organizations can profit from cloud computing by adopting a systematic and effective validation approach to ensure their applications match the essential requirements, regardless of the difficulties in integrating and validating cloud apps.

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Smart Agriculture Using Modern Technologies

Smart Agriculture Using Modern Technologies

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： N Shanmugasundaram G Santhip Kumar S Sankaralingam S Vishal N Kamaleswaran Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

Agriculture is a critical resource for survival of all living things on earth. Agriculture also provides most of the food and fabrics to the world. Environmental changes have impacted today’s agriculture and it has slowed down the productivity of crops. Shortage of water and pests are the major issue that mitigates the yield in agriculture. To sort out these issues, experts and researchers have proposed new processes and adoption of new technologies like Internet of Things (IoT), Smart sensors, Wireless Sensor Networks (WSN), Image processing, Data analytics, Artificial Intelligence (AI) and Machine learning (ML) in agriculture in the last decade to make agriculture smart, easy and maximize the production with minimum resources. This paper studies the impact of new technologies and its applications in modern agriculture.

关键词： Smart agriculture Productivity Wireless sensor networks Image processing process control Machine learning Agriculture

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Improving Teamwork in Software Engineering Projects in Higher Education 53

Improving Teamwork in Software Engineering Projects in Highe...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Quek, Ser Wee Darren Fu Teo, Jared Kai Louahemmsabah, Nasruddine Cyrus Tong, Yap Boon Rong Poh, Aaron Zheng Cao, Qi Seow, Chee Kiat Yau, Peter Chunyu Chen, Alex Qiang School of Computing Science University of Glasgow United Kingdom Singapore Institute of Technology Infocomm Technology Cluster Singapore

ISBN: (纸本)9798350336429

In computing science (CS) education, software programming and software engineering subjects are parts of core CS subjects where students learn various programming languages and software development life cycle (SDLC). With the aim of equipping students with the necessary project skills and experiences essential for workplaces, Institutes of Higher Learning (IHL) have increasingly adopted the educational practice of bringing real-world team-based projects in the curriculum. Such software projects commonly support the use of Agile process practices, which place more emphasis on communications and teamwork among students in a team, stakeholders and industry customers. This could be manageable for small scale projects where student teams are in their learning journey with active and continuous communications with customers, while developing the necessary cohesion within the team to facilitate incremental deliverables of their software products. However, teamwork remains a bottleneck that is difficult to be consistently realized among members in group projects. It is not easy to motivate and measure all team members working towards the common goals or complete project tasks on time. As such, this paper proposes the Weekly Team Assessment (WTA) framework to continuously encourage teamwork throughout the software project development duration. We also propose the settings of dedicated leadership and constructive feedback iteratively in the WTA framework. The proposed methods have been applied and evaluated through the experiments in two software engineering modules, Professional Software Development and Team Project, involving 50 sophomore CS students who are divided into control group and experimental group equally. Experiment results are analyzed and discussed in this paper. Positive outcomes are observed from the experiment where about 72% of participants appreciate the proposed methods in improving their teamwork in group projects. The degree of effectiveness of the prop

关键词： higher education software development life cycle software engineering education team projects Teamwork

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Flower Pollination Heuristics for Nonlinear Active Noise control Systems

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Computers, Materials & Continua 2021年第4期67卷 815-834页

作者： Wasim Ullah Khan Yigang He Muhammad Asif Zahoor Raja Naveed Ishtiaq Chaudhary Zeshan Aslam Khan Syed Muslim Shah School of Electrical Engineering and Automation Wuhan UniversityWuhan430072China Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Electrical Engineering International Islamic UniversityIslamabadPakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabadPakistan

In this paper,a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control *** recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal,random and complex random signals as noise *** flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra *** comparative study on statistical observations in terms of accuracy,convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable,accurate,stable as well as robust for active noise control *** accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms,particle swarm optimization,backtracking search optimization algorithm,fireworks optimization algorithm along with their memetic combination with local search ***,the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.

关键词： Active noise control computational heuristics volterra filtering flower pollination algorithm

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Performance Comparative Analysis of Artificial Intelligence Chip Technology 2

Performance Comparative Analysis of Artificial Intelligence ...

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2nd international conference on Computer Engineering and Intelligent control (ICCEIC)

作者： Gao, Ruize Song, Mo Jilin Univ Coll Elect Sci & Engn Jilin Jilin Peoples R China Washington Univ Dept Elect & Syst Engn St Louis MO 63110 USA

ISBN: (纸本)9781665402125

The rapid development of artificial intelligence (AI) demands the enhancement of domain-specific hardware specifically designed for AI applications. Although the depth of hardware acceleration methods has been dug deeply to tickle this problem, a comprehensive review is not enough to get a detailed knowledge of these cutting-edge techniques. This paper proposes the direction and challenges of different chip designs in the present. In general, the AI chip refers to a chip that has been specially designed for and used in the field of AI algorithms. The frameworks and characteristics of the hardware accelerators based on conventional von Neumann architecture are described as the basic methods. In addition, the amount of data has shown explosive growth, where the traditional computing architecture cannot support the massive transition requirement of the data between computing processor and memory. Inference efficiency becomes the major design consideration for modern machine learning accelerators. The throughput and energy consumption were mainly determined by the efficiency of performing multiply-and-accumulate (MAC) during inference, and new insight to address this problem was provided by a process-in-memory (PIM) method. Finally, inspired by the physiological principle, a pathway of brain-inspired computing becomes an energy-efficient approach to AI computing workloads. The development of various brain-inspired chips based on non-volatile memory is reviewed in this paper, focusing on computing density, energy efficiency, computing accuracy, on-chip learning capability, and co-design principles, from the device to the algorithm level.

关键词： AI Chip GPU FPGA ASIC In-Memory computing Brain-Inspired computing

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Hadoop-based Secure Storage Solution for Big Data in Cloud computing Environment using Elliptical Curve Diffie - Hellman Algorithm

Hadoop-based Secure Storage Solution for Big Data in Cloud C...

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Data Science and Network Security (ICDSNS), international conference on

作者： Prakash Murugesan Venkata Arun Kakumanu Artificial Intelligence and Data Verizon USA Data Engineering Fusion Plus Solutions Inc USA

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In recent trends, data security is considered one of the most crucial portions of big data analysis. The applications of big data manage sensitive data including medical, business, and personal records. One of the largest software systems for distributing data to process and manage huge quantities is Hadoop. Threats could comprise the cloud system utilized to store the types of cloud data. However, the primary problem of the Hadoop application is unauthorized access and took a long time to compute the data. In this research, the Elliptic Curve-Diffie-Helman (ECDH) algorithm is implemented to increase the encryption process and reduce the computation amount. First, utilize the HDFS federation and high-availability techniques to distribute the NameNode service between the servers. Then, a Zookeeper distribute coordination mechanism is employed to coordinate each node to achieve secure storage. Next, the ECDH encrypts the consistent data to adopt a homomorphic encryption algorithm which encrypts the computed data. After the ECDH process, the Perfect Forward Secrecy (PFS) encryption mode is applied to speed up the encryption process. At last, the HDFS control module design incorporates the storage model and encryption method. From the result analysis, it clearly shows that the proposed ECDH algorithm with PFS achieves better encryption time, data upload rate, and running time when compared to existing Improved Aprior and Dual Thread-Elliptic Curve Cryptography (DH-ECC).

关键词： Cloud computing Memory Big Data Metadata Network security Software systems Servers Sparks Homomorphic encryption Secure storage

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Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning

Identifying Ordinary Differential Equations for Data-efficie...

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international Joint conference on Neural Networks (IJCNN)

作者： Tobias Nagel Marco F. Huber Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system’s governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.

关键词： Knowledge engineering Machine learning algorithms Neural networks process control Reinforcement learning Ordinary differential equations Mathematical models

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Proceedings of DAAC 2019: 3rd Industry/University Joint international Workshop on Data-Center automation, Analytics, and control - Held in conjunction with SC 2019: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of DAAC 2019: 3rd Industry/University Joint Inte...

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3rd IEEE/ACM Industry/University Joint international Workshop on Data-Center automation, Analytics, and control, DAAC 2019

ISBN: (纸本)9781728159911

The proceedings contain 5 papers. The topics discussed include: MELA: a visual analytics tool for studying multiï¬üdelity HPC system logs;MetricQ: a scalable infrastructure for processing high-resolution time series data;hiperjobviz: visualizing resource allocations in high-performance computing center via multivariate health-status data;multi source cooling control algorithm;and HyperOXN: a novel optical cross-connect architecture for data centers.

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