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SSRN 2023年

作者： Iqbal, Muhammad Ijaz, Muhammad Mazhar, Tehseen Shahzad, Tariq Abbas, Qamar Ghadi, Yazeed Yasin Ahmed, Waseem Hamam, Habib Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science FCIT International Islamic University Islamabad Pakistan Department of computer science and software engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Computer Science and Information Technology University of Malakand Pakistan Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering. Science University of Johannesburg Johannesburg2006 South Africa Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia

Objctives This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy. Methods To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. Results The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition. © 2023, The Authors. All rights reserved.

关键词： Software design

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An Enhanced Haar Cascade Face Detection Schema for Gender Recognition

An Enhanced Haar Cascade Face Detection Schema for Gender Re...

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Smart Computing and Application (ICSCA), International Conference on

作者： PSVS Sridhar Premkumar Chithaluru Sunil Kumar Omar Cheikhrouhou Habib Hamam Computer Science and Engineering Koneru Lakshmaiah Education Foundation (KLEF) Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology (A) Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India CES Laboratory National School of Engineers of Sfax University of Sfax Sfax Tunisia Faculty of Engineering University de Moncton Moncton NB Canada 5Higher Institute of Computer Science of Mahdia University of Monastir Mahdia Tunisia International Institute of Technology and Management Commune d'Akanda Libreville Gabon Department of Electrical and Electronic Engineering Science School of Electrical Engineering University of Johannesburg Johannesburg South Africa Spectrum of Knowledge Production and Skills Development Sfax Tunisia

Gender identification software is becoming increasingly popular in the age of machine-to-machine interactions, as the need for person-aligned, effective, and ethical systems become more apparent. In this age of machine-to-machine interactions, automated gender identification software is becoming more popular. The bulk of the systems for gender identification that were examined used either textual or audiovisual data as input to determine their gender. Many different approaches to automatically detect gender based on traits gathered from people's bodies and/or behaviors have been suggested, and there are simply too many to include here. The accuracy of automated gender identification, on the other hand, has always been a topic of worry or a source of criticism. Initial face identification is accomplished by the use of the Haar Cascade face detection method, which is based on the Viola-J ones face recognition algorithm. Facial characteristics such as the eyes, mouth, and nose are then identified. Second, using the Viola-Jones face detection algorithm, the faces and facial characteristics such as the eyes, mouth, and nose are recognized, and the results are shown. In order to increase the accuracy of gender identification, adaptive filters are employed to reduce the amount of noise present before the detection process is carried out. The facial qualities that have been gathered are utilized as input or test data for the neural network, which is subsequently trained on these characteristics. It is intended that the neural network acquires the features of the genders and then functions as a classifier when it comes to detecting them. Instead of saying it another way, a neural network known as Keras is used for feature extraction and gender identification, which is free and open-source software.

关键词： Ethics Face recognition Neural networks Nose Mouth Feature extraction Behavioral sciences

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Impact of Hyperparameter tuning on Solar PV Performance using Machine Learning for Moscow, Russia

Impact of Hyperparameter tuning on Solar PV Performance usin...

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International Conference on Soft Computing and Measurements (SCM)

作者： Kingsley Okoli Onah Uche Haastrup Adebayo Ibukun Victor Achirgbenda Innocent Joseph Department of Computer Science and Knowledge Discovery Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia Department of Photonics Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia Department of Mechanical Engineering Federal University of Technology Akure Ondo State Nigeria Department of Mechanical Engineering University of Agriculture Makurdi Benue State Nigeria

The usage of solar photovoltaic (PV) systems has become a viable option for supplying the worlds expanding energy needs while lowering greenhouse gas emissions. The efficiency and cost-effectiveness of the system must be designed and optimized, which requires accurate modeling of solar PV performance. In order to choose the best hyperparameters for a particular situation, it is important to have a deeper grasp of how hyperparameter tuning affects solar PV model performance. In this paper, the impact of hyperparameter tuning on the effectiveness of solar PV modeling for Moscow, Russia, using machine learning approaches is investigated. For the purpose of predicting the performance of the solar PV system, the data and various machine learning models are being analyzed. These models performance is assessed using a variety of criteria while contrasting its results during hyper tuning. The accuracy and dependability of the solar PV performance models can be greatly increased, according to the research, by hyperparameter tuning. We outline the implications and prospective effects of our study on the design and optimization of solar PV systems in Moscow, Russia, and make suggestions for further study. The findings of this study would help Russia and other regions design solar PV systems that are more effective and sustainable.

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‘Online grazing permit’: A review of novel digitalisation procedures of natural resources management projects in Iran

World Development Sustainability

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World Development Sustainability 2024年 5卷

作者： Amiraslani, Farshad Mortazavi Sarmad, Elham Fellow The Iran's Vice-Presidency for Science Technology and Knowledge-based Economy Department of RS/GIS Faculty of Geography University of Tehran Tehran Iran School of Computer Science and Electronic Engineering University of Essex Essex CO4 3SQ United Kingdom

Globally, there is a constant growth in digitalisation and e-services. Their main benefits include higher transparency, reliability and accountability of service providers while strengthening fair competition and collaboration. This article improves upon previous literature by adding a new innovative dimension to natural resources management through digitalisation processes. It also narrates a relatively swift alteration of public policies in response to growing access demands to digital data. Here, we examine one of the well-established Iranian public organisations for natural resources management, called 'Forest, Rangeland and Watershed Management Organisation'. This organisation has shifted its procedures to become a significant electronic service provider within a relatively short period. Given its extended offices, diverse range of activities, and human resources portfolio, it could become a role model for other similar organisations at the national, regional and global levels. This review paper highlights the challenges of forming a national digital-based authority for natural resource management. We will cover a few remarks on such quick transition strengths and failures. The lessons learned can be considered in other similar contexts. © 2024 The Author(s)

关键词： Digitalisation FRWO Iran natural resources management Public services

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Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv

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arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

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UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

arXiv

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arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Advancing Wire Arc Additive Manufacturing: A New Era of Precision and Efficiency in Large Metal Component Production

Advancing Wire Arc Additive Manufacturing: A New Era of Prec...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Irfan Khan Shaik Anjimoon V Asha Atul Singla P. Akhil Nahed Mahmood Ahmed Alsultany Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India Collage of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

A comprehensive Wire Arc Additive Manufacturing (WAAM) approach that employs five algorithms to improve massive metal components is described in this research. The adaptive layer thickness control algorithm adjusts layer thickness based on real-time complexity. This ensures additive manufacturing accuracy throughout. Also, the Material Versatility Optimization Algorithm simplifies working with multiple materials, making casting more efficient. The Intelligent Path Planning Algorithm allocates the robotic arm’s movement to traverse the shortest distance and uniformly distribute heat during deposition. The MultiMaterial-Materialon Strategy Algorithm improves material amounts, making WAAM more usable for additional materials. The Real-Time Excellent Assurance Algorithm constantly examines and adjusts parameters to ensure excellent production. A complete analysis of research reveals how algorithms are related and how vital they are for accuracy, speed, and flexibility. Testing shows that the recommended technique controls layer thickness, material variety, deposition velocity, accuracy, energy efficiency, and real-time tracking better than typical WAAM methods. Figures 6, 7, and 8 indicate that the proposed method delivers superior results across several criteria. The recommended WAAM approach can create more massive metal components by fundamentally changing how things are done. This research provides a comprehensive additive manufacturing approach to solve numerous large-scale metal production issues.

关键词： Technological innovation Accuracy Metals Production Three-dimensional printing Real-time systems Path planning

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Machine Learning-Driven Blockchain for Enhanced Drug Discovery and Development in Pharmaceutical Research

Machine Learning-Driven Blockchain for Enhanced Drug Discove...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： V Asha Shaik Anjimoon Meenu Rani Verma Atul Singla Irfan Khan Mohammed Ayad Alkhafaji Department of Computer Applications New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad DoCSBS GyanGanga Institute of Technology and Sciences Jabalpur M.P India Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Machine learning (ML) and blockchain technologies have made huge steps forward in the pharmacy business by being used in the process of creating and discovering new medicines. Both breakthroughs were made possible by using both tools together. With the help of a blockchain design powered by machine learning, the study’s main goal is to come up with a new way to make drug research more effective and efficient. This method makes it possible to make molecules with a wide range of structure features that have the right pharmacological qualities. You can also make these molecules, improve the plans of clinical trials, and guess how drugs will interact with their targets. Technologies like generative adversarial networks, reinforcement learning methods, and deep learning algorithms have made it possible for these things to be used. If blockchain technology is used throughout the whole process of making medicines, it improves global openness, traceability, and data security. This is because it makes sure that managing data is safe. The tests show that the suggested method works better than the others when it comes to the process of finding and developing new drugs. This is better because the creation process is more precise, accurate, and efficient.

关键词： Drugs Deep learning Technological innovation Data security Reinforcement learning Production Clinical trials

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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

arXiv

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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The computational complexity of angry birds 29

The computational complexity of angry birds

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Stephenson, Matthew Renz, Jochen Ge, Xiaoyu Department of Data Science and Knowledge Engineering Maastricht University Netherlands Research School of Computer Science Australian National University Canberra Australia

ISBN: (纸本)9780999241165

In this paper we present several proofs for the computational complexity of the physics-based video game Angry Birds. We are able to demonstrate that solving levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard, depending on the maximum number of birds available and whether the game engine is deterministic or stochastic. We believe that this is the first time that a single-player video game has been proven EXPTIME-hard. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Birds

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