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arXiv 2023年

作者： Al-Obaidi, Fatin E.M. Al-Saleh, Anwar H. Kafi, Shaymaa H. Karam, Ali J. Al-Zuky, Ali A.D. Dept. of Physics Mustansiriyah University Iraq Dept. of Computer Science Mustansiriyah University Iraq Dept. of Information Technology Garmian University Iraq

The normalized 2-D correlation technique is a robust method for detecting targets in images due to its ability to remain invariant under rotation, translation, and scaling. This paper examines the impact of translation, and scaling on target identification in images. The results indicate a high level of accuracy in detecting targets, even when they are exhibit variations in location and size. The results indicate that the similarity between the image and the two used targets improves as the resize ratio increases. All statistical estimators demonstrate a strong similarity between the original and extracted targets. The elapsed time for all scenarios falls within the range (44.75-44.85), (37.48-37.73) seconds for bird and children targets respectively, and the correlation coefficient displays stable relationships with values that fall within the range of (0.90-0.98) and (0.87-0.93) for bird and children targets respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Harshit Agarwal Parth Middha Ayush Thakur Sofia Singh Shipra Saraswat Neha Bhatia Dept. of Computer Science & Engineering Amity University Noida India Amity Institute of Information Technology Amity university Noida India Dept. of Information Technology Panipat Institute of Engineering & Technology Panipat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence.

关键词： Training Technological innovation computer vision Accuracy Scalability Neural networks Benchmark testing Natural language processing Communications technology Optimization

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Machine Learning in Agriculture and Crop Management: A Comprehensive Review

Machine Learning in Agriculture and Crop Management: A Compr...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rachna Rathore Archana Ganesh Said Preeti Jnagra Haritha Murari Saloni Bansal Harshita Singh Dept. of Mathematics Jiwaji University Gwalior India Dept. of Computer Engineering AISSMS’s Institute Of Information Technology Pune India Dept. of Biotechnology Lingayas Vidyapeeth Haryana India Dept. of Software Engineer Spark Infotech Inc Phoenix Arizona USA Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Machine learning (ML) has transformed agriculture and crop management, revolutionizing modern farming techniques. By leveraging ML, farmers can accurately monitor and analyze agricultural data, gaining valuable insights into crop health, soil conditions, weather patterns, and pest infestations. Through the analysis of satellite imagery, sensor data, and historical records, ML models can predict optimal planting times, forecast yields, and identify potential disease outbreaks, enabling more efficient resource management. These innovations promote precision agriculture, reduce waste, and support sustainability. This paper explores the latest developments in ML applications for agriculture, emphasizing their potential to enhance food security, minimize environmental impact, and boost farm profitability through data-driven decision-making.

关键词： Technological innovation Systematics Reviews Weather forecasting Machine learning Predictive models Soil Satellite images Sustainable development Crop yield

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An Automatic Generation of Test Cases in JUnit Testing Framework Using Genetic Algorithm

An Automatic Generation of Test Cases in JUnit Testing Frame...

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International Conference on Systems and Control (ICSC)

作者： Mabroukah Amarif Aisha Alfitouri Zaydan Allag Dept. of Information Systems Faculty of Information Technology Sebha University of Libya Sebha Libya Dept. of Computer Sciences Faculty of Information Technology Sebha University of Libya Sebha Libya Dept. of Computer Sciences Information Technology Faculty Sebha University of Libya Sebha Libya

The testing phase is one of the most important phases of the software life cycle, which ensures the efficiency and quality of the software product. Many tests are conducted on the software, not only to detect errors, but also to provide a complete picture of the quality of the outputs provided by the program and to increase confidence in all of the functions. Due to the difficulty in estimating most of the test data and the time spent in this process, many techniques are used in the automatic generation of test cases. The genetic algorithm is one of the most important used methods in this process, and it has achieved great success in generating limited and appropriate test cases to give a complete conceptualization of the function under test. The Junit testing framework is used in automated testing processes within test-based development methodologies, but it lacks of a mechanism to specify and generate a specific test data that put the program under test in the required scenario. In order for the programmer to be able to perform the test automatically in an integrated way within JUnit testing framework, the genetic algorithm has been improved in this research and emerged into the JUnit testing framework to fully automatic testing and generate various numeric test cases at the unit-test stage. Based on testing of the proposed algorithm within the Junit testing framework, it has proven its effectiveness and facilitation of generating perfect specific numeric test cases.

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A novel CNN method for the accurate spatial data recovery from digital images

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Materials Today: Proceedings 2023年 80卷 1706-1712页

作者： Murugan, G. Moyal, Vishal Nandankar, Praful Pandithurai, O. John Pimo, Er.S. St John College Of Engineering and Management Maharashtra Palghar India SVKMs Institute of Technology Maharastra Dhule India Electrical Engineering Dept. Government College of Engineering Nagpur India Computer Science and Engineering Rajalakshmi Institute of Technology Poonamallee Tamilnadu Chennai India Department of CSE St. Xavier's Catholic College of Engineering Chunkankadai Tamil Nadu Nagercoil India

The parsing of floorplans has been an issue for a long time in automated document processing and with algorithmic methods until recent years. This problem has also improved output with the emergence of convolutionary neural networks (CNN). The job here is to obtain spatial and geometrical data from floorplans as accurately as possible. The aim of this project is to extract the most information from a floor plan image around instance segmentation models like Cascade Mask R-CNN. A new style of key point CNN is being implemented to supplement the segmentation to find correct corner positions. Then the resulting segmentation is combined in a post-processing stage. With a mean IoU of 72.7 percent versus 57.5 percent, the resulting segmentation scores surpass the existing baseline of the CubiCasa5k floorplan data base. Moreover, for almost every class, the mean IoU for each class is increased. Cascade Mask R-CNN has also been shown to be better suited to this role than Mask R-CNN. © 2021

关键词： E-learning

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Improving Network Efficiency in Clustered Tree Topology through PSO Optimization in IEEE 802.15.4-DSME based IoT Networks

Improving Network Efficiency in Clustered Tree Topology thro...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Sonali Anand Nikumani Choudhury Tamoghna Ojha Anakhi Hazarika Jay Dave Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Mathematics & Computing Indian School of Mines Indian Institute of Technology Dhanbad India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPANs). Deterministic and Synchronous Multi-channel Extension (DSME) is one of the key Medium Access Control (MAC) modes of the IEEE 802.15.4 standard that was introduced to address the need for higher reliability, lower latency, and better scalability in network communication in Internet of Things (IoT) applications. However, in dynamic network conditions (topology and channel traffic), the network suffers from several limitations, such as low packet delivery ratio, high latency and packet collisions, and finally, higher power consumption. This is because of sub-optimal parameter settings. The standard does not define any mechanism to adapt the parameters based on network conditions. In this paper, we propose a mechanism to adapt/fine-tune the network parameters DSME along with the optimization of QoS using an algorithm based on Particle Swarm Optimization(PSO) for dense and dynamic DSME-based IoT networks. Through simulations, the proposed mechanisms’ performance is analyzed in terms of energy efficiency, transmission overhead, throughput, and latency and is shown to outperform the existing DSME MAC scheme.

关键词： IEEE 802.15 Standard Network topology Heuristic algorithms Wireless personal area networks Throughput Topology Telecommunications Internet of Things Standards Optimization

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Development of a teaching model based on lifelong learning through active methodological strategies 12

Development of a teaching model based on lifelong learning t...

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12th International Conference on Virtual Campus, JICV 2022

作者： Trujillo-Aguilera, F D. Blazquez-Parra, E.B. Almendros, J. Calderon Marti-Campoy, A. Plaza-Garcia, I. University of Malaga Dept. Electronic Technology Malaga Spain University of Malaga Dept. Graphic Engineering Design and Projects Malaga Spain Polytechnic University of Valencia Dept. of Computer and Systems Informatics Valencia Spain University of Zaragoza Dept. of Electronic Engineering and Communications Zaragoza Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in which a set of active teaching activities have been implemented for the acquisition of key competences in the challenge of achieving one of the most important goals that any educational system must provide: lifelong learning, thereby facilitating the incorporation of new university graduates into the world of work. This project has been implemented on the subject of Power Electronics. However, to facilitate the transfer to other subjects, the catalogue of active teaching activities is sufficiently varied to make it easy to extrapolate and use in other subjects, thanks to the fact that teachers have a wide variety of activities to choose from the set. Finally, a survey has been drawn to evaluate this new active methodology's satisfaction, impact and improvement to know the students' perspectives once they have worked with this catalogue of activities. © 2022 IEEE.

关键词： E-learning

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shingchern D. You Chien-Hung Liu Ching-Cheng Yen Dept. of Computer Science and Information Engineering National Taipei Universiyt of Technology Taipei Taiwan

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach.

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Transforming Brownfield Factories: Unleashing the Potential with Co-Engineering and Virtual Commissioning

Transforming Brownfield Factories: Unleashing the Potential ...

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Annual Conference of Industrial Electronics Society

作者： Oskar Wintercorn Khalid Atta Peter Jeppsson Cristina Paniagua Jan Van Deventer Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

Manufacturing lines are subject to continual change as components inevitably wear down over time, necessitating replacement. Often, the exact original parts may no longer be readily available, or an upgrade to integrate new technology becomes desirable. The crux of these scenarios lies in swiftly and seamlessly incorporating these changes, minimizing disruption to the existing processes. To address this, we put forward a concept utilizing a digital twin, complemented by an open-source industrial IoT framework. This combination facilitates both software and hardware loop integration, paving the way for efficient co-engineering development with existing legacy systems. Through this approach, we enable swift technological integration, keeping the manufacturing process agile and up-to-date.

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