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2nd IEEE International Conference on Cyberspace, CYBER NIGERIA 2020

作者： Ogu, Reginald E. Ikerionwu, Charles I. Ayogu, Ikechukwu I. Dept. of Software Engineering Federal University of Technology Owerri Owerri Nigeria Dept. of Computer Science Federal University of Technology Owerri Owerri Nigeria

ISBN: (纸本)9781665444095

The integration of more sensory and actuation components to the Smart Grid produces high volume of data. Consequently, this big data stretches the transmission, processing, and storage capabilities of the Smart Grid infrastructures. The vulnerability of advanced metering infrastructures (AMIs) is on the rise, as more devices are connected to the Internet these days. The aforementioned realities have continued to necessitate a debate on the future of cloud-centered artificial intelligence (AI) services for latency-sensitive user-centric IoT applications. It is rapidly becoming necessary to leverage on the applicability of EdgeAI directly on IoT sensory nodes involved in energy metering. This paper proposes the applicability of Artificial Intelligence situated on smart meter, to perform micro analytics at the edge of AMI networks: Artificial Intelligence of Things. Therefore, a functional AMI model based on IoT and EdgeAI is presented herein. Additionally, an integration architecture for the anticipated Smart Grid based on IoT and EdgeAI is presented. On implementation, the proposed model would provide high performance analytics and Edge computing capabilities to enable AMIs initiate instant data check at the source and relay relevant real-time data to the Utility through the Internet. © 2021 IEEE.

关键词： Energy utilization

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CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms

CapsiScan: Autonomous Robotic System for Monitoring and Mana...

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Advancements in Computing (ICAC), International Conference on

作者： E.R.T. Jayakody W.T.T. Rabel R.D.V. Estrop K.G.L.P. Herath S. M. B. Harshanath Pradeep K. W. Abeygunawardhana Dept. of Computer Systems Engineering Sri Lanka Institute Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Colombo Sri Lanka

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions.

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IoT Based Smart Automated Agriculture and Real Time Monitoring System 2

IoT Based Smart Automated Agriculture and Real Time Monitori...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shamrat, F. M. Javed Mehedi Hossain, Alamin Roy, Tonmoy Adeeb Khan, Md Ahasanul Khater, Ankit Rahman, Md Tareq Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh Dalhousie University MEng In Internetworking Halifax Canada Jadavpur University Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665433686

In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. This research work has designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Le vel Detection Module (WLDM) always detect the water level to avoid drops destruction;Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump;Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan;Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land. © 2021 IEEE.

关键词： Soil moisture

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Intelligent Agriculture Robot for Tea Plantation Preservation - TeaBot

Intelligent Agriculture Robot for Tea Plantation Preservatio...

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IEEE International Conference on Multi Topic

作者： Imalka Gunawardana Varnavi Perera Thulya Premathilake Gayan Bamunusinghe Shashika Lokuliyana Narmada Gamage Rajitha de Silva Dept. of Software Engineering SLIIT Malabe Sri Lanka Dept. of IT SLIIT Malabe Sri Lanka Dept. of Computer Systems Engineering SLIIT Malabe Sri Lanka Lincoln Agri-Robotics University of Lincoln United Kingdom

Tea cultivation stands out as one of the most significant export products that contribute to the Gross Domestic Product (GDP). In the previous decades, the substantial work-force gradually transitioned to different occupations. Compared to other crops, tea cultivation demands costly and meticulous upkeep. With a shortage of labor, maintaining tea estates grew tougher, leading to decreased yields. In reaction, estate owners transitioned to cultivating low-maintenance crops. The TeaBot is an advanced robot designed to replace human labor for watering and fertilizing vast tea estates. The TeaBot distinguishes itself by operating in rough outdoor terrain and infrastructure-free navigation in real tea plantations. Given that tea plants demand continuous hydration and nutrients for optimal crop yield, TeaBot plays a pivotal role in enhancing efficiency while reducing water and fertilizer wastage. Mainly four motor-powered wheels move accurately by translating linear and angular velocities using a precise motor control algorithm. An autonomous navigation algorithm was developed using two distinct approaches, which include deep learning-based computer vision and classical computer vision. The selection of the classical computer vision method was predicated upon its notable attributes, including high precision, minimal resource utilization, and optimal efficiency. A deep learning-based stem identification model was trained based on MobileNetV2 architecture to detect where the plant stem meets the ground for efficient hydration of individual plants. This lightweight model achieved 90% detection accuracy. The precise results of stem detection have made the liquid fertilization process more efficient.

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A Meta-Model for Outcome-Based Education: Streamlining Evaluation Processes

A Meta-Model for Outcome-Based Education: Streamlining Evalu...

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Asia-Pacific Conference on software engineering

作者： Muhammad Awais Muhammad Waseem Anwar Farooque Azam Dept. of Computer Software Engineering CEME NUST Islamabad Pakistan Dept. of Innovation Design and Eng. Mälardalen University Västerås Sweden

Outcome-Based Education (OBE) is widely recognized for its goal-oriented approach in education. The key elements of outcome-based education are learning outcomes which are measured at student, course, program, and institution levels. The evaluation of learning outcomes is usually performed manually in isolation which leads to several issues like assessment delays and impaired judgement. Furthermore, integration of OBE techniques with existing Learning Management System (LMS) becomes impracticable. To handle such issues, in this article, we propose a framework comprising Metamodel for streamlining evaluation processes of OBE. Moreover, a set of text-to-model transformations is implemented for the automatic gen-eration of high-level models from traditional documents containing information about students, courses, grades etc. Furthermore, the model-to-text transformations are implemented to generate the target model in JAVA for the assessment of learning outcomes. This facilitates OBE evaluation straightforwardly. The efficacy of the framework is validated through a case study conducted within the Department of software engineering at University of AJK. The results are encouraging, and OBE assessment is successfully performed for software engineering courses.

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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Deep Learning-based Spectral Efficiency Maximization in Massive MIMO-NOMA Systems with STAR-RIS

Deep Learning-based Spectral Efficiency Maximization in Mass...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Science and Engineering International University - Vietnam National University HCM City Vietnam School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

This paper studies a deep learning-based framework for spectral efficiency maximization problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) systems with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). We formulate the spectral efficiency maximization with a joint design of power allocation of the users, phase shift matrix of transmission and reflection element at the STAR-RIS. Since the problem is non-convex and power allocation of the users and reflector/transmitter elements at a STAR-RIS are coupled, it is very challenging to solve optimally. We propose a low-complexity iterative algorithm based on the inner approximation (IA) method to solve this problem with guaranteed convergence at a relatively optimal level. For real-time optimization, we design a deep learning (DL) framework to predict the optimal solution of power allocation of users, phase shift matrix of transmission and reflection elements at the STAR-RIS according to distances and channel gains from the base station (BS) to STAR-RIS and from STAR-RIS to users. Simulation results show that the suggested scheme improves the spectral efficiency (SE) compared to the massive MIMO system with direct link and without STAR-RIS. Besides, the DL framework can predict the optimal solution within a short time under the suggested scheme.

关键词： Deep learning Base stations Spectral efficiency Simulation Massive MIMO Reflection Real-time systems

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Transformer Models for Enhanced Natural Language Processing in Medical Records Management

Transformer Models for Enhanced Natural Language Processing ...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Vaibhav Prakash Vasani Swapnil Chandrakant Pawar Sowmya P. Shahanawaj Ahamad Aradhana Sahu Gokul Talele Dept. of Computer Engineering Somaiya Vidyavihar University Mumbai Maharashtra India Dept. of CSE Nitte (Deemed to be University) NMAMIT Nitte India Dept. of Software Engineering College of CSE University of Hail Hail City Saudi Arabia Dept. of CSE Rungta College of Engineering and Technology Bhilai Chhattisgarh India Lead Data Scientist Accenture Pune India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The use of transformer models to enhance NLP in MR administration is investigated. Renowned for their ability to understand contextual links along with efficiently handle large datasets, transformer designs provide clear improvements in the processing along with analysis of medical language. Work improves the extraction, classification, along with summarising of data from complex medical records by use of models like BERT, GPT, along with variants. The study looks at how well these models may simplify data retrieval, increase the accuracy and relevance of CR, along with support decision making in medical environments. The results imply that the use of transformer-based NLP techniques might greatly boost the medical record administration, thereby improving patient care, accelerating procedures, along with optimising data usage. Transformer models such as BERT are used in the research approach aiming at enhancing NLP in medical record handling. Literature, preprocessing EHRs, fine-tuning and assessment along with pilot testing all plays a part. Maintaining ethical issues, a user interface is given for interaction. This method improves medical data handling effectiveness. BERT has showed promise in handling medical records in terms of enhancing identification and categorisation of medical entities found in data. Various medical entities including diseases, medications, procedures, and symptoms have been specifically tailored into the strong language paradigm. Confusion matrix performance measures reveal how well model controls medical records. Future studies could investigate combining BERT with other advanced NLP methods, extending dataset to include a wider range of medical records, using TLT in case of particular medical domains, including real time data processing and feedback systems, and investigating ethical issues along with data privacy concerns in health care.

关键词： Ethics Computational modeling Medical services User interfaces Transformers Natural language processing Data models Real-time systems Testing Diseases

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ROBUSTNESS OF DYNAMICS IN GAMES: A CONTRACTION MAPPING DECOMPOSITION APPROACH

arXiv

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arXiv 2023年

作者： Arefizadeh, Sina Arefizadeh, Sadegh Etesami, S. Rasoul Bolouki, Sadegh Dept. of Electrical and Computer Engineering Arizona State University TempeAZ United States Dept. of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Dept. of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign ChampaignIL United States Dept. of Mechanical Engineering Polytechnique Montréal MontrealQC Canada

A systematic framework for analyzing dynamical attributes of games has not been well-studied except for the special class of potential or near-potential games. In particular, the existing results have shortcomings in determining the asymptotic behavior of a given dynamic in a designated game. Although there is a large body literature on developing convergent dynamics to the Nash equilibrium (NE) of a game, in general, the asymptotic behavior of an underlying dynamic may not be even close to a NE. In this paper, we initiate a new direction towards game dynamics by studying the fundamental properties of the map of dynamics in games. To this aim, we first decompose the map of a given dynamic into contractive and non-contractive parts and then explore the asymptotic behavior of those dynamics using the proximity of such decomposition to contraction mappings. In particular, we analyze the non-contractive behavior for better/best response dynamics in discrete-action space sequential/repeated games and show that the non-contractive part of those dynamics is well-behaved in a certain sense. That allows us to estimate the asymptotic behavior of such dynamics using a neighborhood around the fixed point of their contractive part proxy. Finally, we demonstrate the practicality of our framework via an example from duopoly Cournot games. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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