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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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XAI for Understanding the Success in Curbing COVID-19

XAI for Understanding the Success in Curbing COVID-19

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Intelligent Systems and computer Vision (ISCV)

作者： Salah Bouktif Nimmi Kunnath Ali Ouni Dept. of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain UAE École de Technologie Supérieure University of Quebec Montreal Canada

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic.

关键词： COVID-19 Analytical models Pandemics Explainable AI Time series analysis Government Sociology

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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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Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

arXiv

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arXiv 2024年

作者： Oh, Jiyong Raza, Syed M. Mwasinga, Lusungu J. Kim, Moonseong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of IT Convergence Software Seoul Theological University Bucheon Korea Republic of

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emergingfield with numerous applications. Operator-controlled UAV flights and manualstatic flight configurations are major limitations for the wide adoption ofscalability of UAV services. Several services depend on excellent UAVconnectivity with a cellular network and maintaining it is challenging inpredetermined flight paths. This paper addresses these limitations by proposinga Deep Reinforcement Learning (DRL) framework for UAV path planning withassured connectivity (DUPAC). During UAV flight, DUPAC determines the bestroute from a defined source to the destination in terms of distance and signalquality. The viability and performance of DUPAC are evaluated under simulatedreal-world urban scenarios using the Unity framework. The results confirm thatDUPAC achieves an autonomous UAV flight path similar to base method with only2% increment while maintaining an average 9% better connection qualitythroughout the flight. © 2024, CC BY.

关键词： Reinforcement learning

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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E-commerce platform based on Machine Learning Recommendation System 6

E-commerce platform based on Machine Learning Recommendation...

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6th International Multi-Topic ICT Conference, IMTIC 2021

作者： Tahir, Muhammad Enam, Rabia Noor Mustafa, Syed Muhammad Nabeel Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science I.T Karachi Pakistan

ISBN: (纸本)9781665482943

Information overload is one of the potential setbacks to many e-commerce platform users. It is very important to filter the media and the choices that are overwhelming for internet users while making buying decisions using online stores. To solve this problem, recommendation systems are used widely. A recommender system helps users find a product of their own choice by filtering and prioritizing and effectively generating the relevant information to its users. The purpose of a recommender system is to save time and hassle of searching through the World Wide Web, instead it generates specific and relevant content that promotes online transaction and bring satisfaction to the users of e-commerce platforms. The proposed system is an e-commerce platform based on an apparel recommendation system that recommends products on the foundation of the user's preferences. © 2021 IEEE.

关键词： Recommender systems

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Towards An Automatic Construction Of Simulation Scenarios: A Systematic Review

Towards An Automatic Construction Of Simulation Scenarios: A...

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Simulation Winter Conference

作者： Christopher W.H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft Portland OR USA Dept. of Engineering & Technology Management Portland State University Portland OR USA Dept. of Computer Science & Software Engineering Miami University Oxford OH USA

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of ‘what-if’ scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning

Intrusion Detection System (IDS) of Multiclassification IoT ...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Layth Rafea Hazim Abdulrahman Ahmed Jasim Oguz Ata Muhammad Ilyas College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey Dept. of Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency.

关键词： Measurement Privacy Correlation Redundancy Intrusion detection Machine learning Security

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ILP-FORMER: solving integer linear programming with sequence to multi-label learning 24

ILP-FORMER: solving integer linear programming with sequence...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Shufeng Kong Caihua Liu Carla Gomes School of Software Engineering Sun Yat-sen University Zhuhai Guangdong China and Computer Science Dept. Cornell University Ithaca NY School of Artificial Intelligence Guilin University of Electronic Technology Guilin Guangxi China Computer Science Dept. Cornell University Ithaca NY

Integer Linear Programming (ILP) is an essential class of combinatorial optimization problems (COPs). Its inherent NP-hardness has fostered considerable efforts towards the development of heuristic strategies. An emerging approach involves leveraging data-driven methods to automatically learn these heuristics. For example, using deep (reinforcement) learning to recurrently reoptimize an initial solution with Large Neighborhood Search (LNS) has demonstrated exceptional performance across numerous applications. A pivotal challenge within LNS lies in identifying an optimal subset of variables for reoptimization at each stage. Existing methods typically learn a policy to select a subset, either by maintaining a fixed cardinality or by decomposing the subset into independent binary decisions for each variable. However, such strategies overlook the modeling of LNS's sequential processes and fail to explore the correlations inherent in variable selection. To overcome these shortcomings, we introduce ILP-FORMER, an innovative model that reimagines policy learning as a sequence-to-multi-label classification (MLC) problem. Our approach uniquely integrates a causal transformer encoder to capture the sequential nature of LNS. Additionally, we employ an MLC decoder with contrastive learning to exploit the correlations in variable selection. Our extensive experiments confirm that ILP-FORMER delivers state-of-the-art anytime performance on several ILP benchmarks. Furthermore, ILP-FORMER exhibits impressive generalization capabilities when dealing with larger problem instances.

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AI Based Monitoring System for Social engineering 3

AI Based Monitoring System for Social Engineering

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Yapa, K. Udara, S.W.I. Wijayawardane, U.P.B. Kularatne, K.N.P. Navaratne, N.M.P.P. Dharmaphriya, W. G. V. U Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets;and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits. © 2021 IEEE.

关键词： Social networking (online)

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