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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Gou, Haosong Su, Ling Zhang, Gaoyi Huang, Wei Rao, Yunbo Yang, Yang Sichuan Co. Ltd. China Mobile Communications Group Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Customer satisfaction is critical for the telecom operators to gain competitive advantages, and the customer satisfaction index is used to evaluate the customers how satisfied with the services, such as network experience and after-sales service. In this paper, we aim to find the internal relationship between customer satisfaction index and network experience by adopting XGBoost machine learning algorithm to help operators predict the satisfaction score to enhance customer satisfaction. We train a better model by selecting key factors, model training, validating and testing. The final experiment shows our trained model achieves much better performance than usual linear regression model. © 2022 IEEE.

关键词： Customer satisfaction

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SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Pant, Bhairvi Obaidat, Mohammad S. Singh, Shreya Pant, Mudita Wazid, Mohammad Das, Ashok Kumar Hsiao, Kuei-Fang Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Ctr. for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500032 India University of Texas-Permian Basin Computer Science Department OdessaTX 79762 United States

ISBN: (纸本)9798350302561

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name 'SLA-SCA'. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features. © 2023 IEEE.

关键词： Internet of things

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The Future of Crime Prevention: Police Case Analysis Using Machine Learning

The Future of Crime Prevention: Police Case Analysis Using M...

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Advancements in Computing (ICAC), International Conference on

作者： Dharsan. R Krishanthini M Traveena C Anubama L Mahaadikara M.D.J.T. Hansika Sanjeevi Shahikala Chandrasiri Department of Computer Science and Software Engineering (CSSE) Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Law enforcement agencies and police departments face challenges in analyzing and preventing accidents and crimes. This research presents a comprehensive machine learning system, “The Future of Crime Prevention: Police Case Analysis,” to enhance public safety. The system comprises components like accident case analysis, crime analysis, case document classification, and crimes against women analysis. It leverages various machine learning techniques to predict accident causes, analyze criminal cases, automate document classification, and cluster crimes against women. The objectives encompass bolstering public safety, enabling proactive decision-making, anticipating future crime rates, and optimizing document analysis. This research strives to provide effective tools for crime and accident prevention, addressing traditional challenges faced by police departments and crime divisions. In an era of advancing technology, the need for innovative solutions to ensure public safety has never been more crucial. Furthermore, it seeks to bridge the gap between law enforcement agencies and cutting-edge technology, revolutionizing the landscape of crime prevention and public safety.

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Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

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12th IEEE Annual information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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A Survey of DDoS Attacks Detection Schemes in SDN Environment

A Survey of DDoS Attacks Detection Schemes in SDN Environmen...

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2023 IEEE International Conference on computer, information, and Telecommunication Systems, CITS 2023

作者： Rawat, Surabhi Gusain Obaidat, Mohammad S. Pundir, Sumit Wazid, Mohammad Kumar Das, Ashok Singh, Devesh Pratap Hsiao, Kuei-Fang Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India Computer Science Department University of Texas-Permian Basin 4901 E. University Blvd. OdessaTX79762 United States

ISBN: (纸本)9798350336092

Denial-of-service (DoS) attacks are a major concern in the field of Internet technology, and they pose a difficult security challenge. Among the different types of DoS attacks, distributed denial-of-service (DDoS) attacks are especially concerning because they can quickly drain the resources of a target, disrupting their computational and communication capabilities without warning. In response to the severity of this problem, various defense strategies have been developed to defend against DDoS attacks. In this paper, a survey on the various DDoS attacks detection schemes is presented. An overview of the DDoS attack methodology is given. The information on different types of DDoS detection methods in SDN environment is also provided. Then a comparative study of different DDoS attack detection schemes is given, which contains the comparisons of different schemes in terms of their advantages, limitations, accuracy values, and time complexity. © 2023 IEEE.

关键词： Denial-of-service attack

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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CXR Scan:X-Ray Image Scanning Application for Lung Cancer and Tuberculosis 13

CXR Scan:X-Ray Image Scanning Application for Lung Cancer an...

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13th IEEE Annual information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Jayasooriya, A.M.U.J. Wickramasekara, T.M.A.M. Jayasinghe, I.C. Gunaratne, U.A. Weerasinghe, Lokesha Dassanayake, Gaya Thamali Department of Data Science Sri Lanka Institute of Information Technology New Kandy Road Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Insittite of Information Technology New Kandy Road Malabe Sri Lanka Department of Information Technology Sri Lanka Institue of Information Technology Kandy Road Malabe Sri Lanka

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

The initial criterion for identifying lung disorders is chest radiographs. The three major lung illnesses that pose the greatest threat to public health are tuberculosis, pneumonia, and lung cancer. Chest X-ray diagnosis of pulmonary illnesses is a challenging undertaking that requires high experience. In rural places, it can be difficult to locate skilled radiologists. Due to the high frequency of TB and lung cancer radiological similarities, many individuals with lung cancer are initially misdiagnosed as having TB and treated incorrectly. According to a recent WHO survey, millions of people die each year as a result of delayed or incorrect diagnoses of lung diseases. This death rate can be reduced, by early detection of certain disorders. This paper proposes a system with 4 main components;Image processing of chest X-rays to identify the disease using Convolutional Neural networks;Predicting the probability of having LC or TB using multivariate data classification techniques;Recommending medicine and related information to support the decision-making process using gaussian naïve bayes, logistic regression model and decision tree classification methods;Visualizing the X-ray image using Augmented Reality. © 2022 IEEE.

关键词： Convolutional neural networks

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal)

Smart Farmer Education System for Disease Identification, Se...

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International Conference on Industrial and information Systems, ICIIS

作者： Keshara Bandara Samitha Vidhanaarachchi A.A.E. Nanayakkara Kavindu Kulasinghe Sanka Rajapakshe Asela Wickramasinghe Kanishka Yapa Department Of Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department Of Computer Science and software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department of Soil Science Faculty of Agriculture University of Ruhuna Mapalana Kamburupitiya Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed ‘AI-based mobile application system for Brinjal Diseases,’ which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections.

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Privacy-Preserving Short-Term Travel Demand Forecasting Based on Federated Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Du, Runmeng He, Daojing Song, Yan Ding, Zikang Chan, Sammy Li, Xuru Xi'an Polytechnic University School of Computer Science Xi'an710048 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China East China Normal University School of Software Engineering Shanghai200062 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan611731 China City University of Hong Kong Department of Electrical Engineering Hong Kong Shanghai Maritime University College of Information Engineering Shanghai201306 China

Travel demand forecasting (TDF) is a crucial task in route planning, navigation, and scheduling. However, developing models for such tasks requires significant amount of order data, which can pose privacy concerns for users. Federated learning (FL) has addressed privacy concerns related to TDF. However, previous works only consider the data of a single factor to train the model, resulting in a less precise assessment of the demand for on-demand ride services. Moreover, non-independent and identically distributed ( non-IID) data across clients also brings a challenge in personalizing models for TDF tasks when applying FL directly. In this work, a privacy-preserving short-term TDF scheme for non-IID data is proposed to deal with these problems. Our approach analyzes the correlation between various potential influencing factors and travel demand and summarizes the input variables of the FL model to achieve accurate TDF prediction with privacy preservation. Additionally, we provide each client with a personalized model trained on independent and identically distributed (IID) data to compensate for local model weight divergence. Finally, experiments are conducted on the 2022 Shanghai data of the Didi platform as a use case, and the results demonstrate that our scheme effectively improves the model's performance. © 1967-2012 IEEE.

关键词： Differential privacy

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OpenCML: An Open Customizable Modeling Language for Directed Acyclic Graphs

OpenCML: An Open Customizable Modeling Language for Directed...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zhenjie Wei Weiwei Xing Weibin Liu Zhiyuan Zou Genxiang Chen School of Software Engineering Beijing Jiaotong University Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Computer School Beijing Information Science and Technology University Beijing China College of Science Minzu University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Directed Acyclic Graphs (DAGs) are widely utilized across various domains for tasks such as graph algorithms, data flow analysis, program optimization, and machine learning. Representing DAGs using General-purpose Programming Languages (GPLs) or Data Serialization Formats (DSFs) can lead to complex and obscure expressions, making it challenging to comprehend and manage the codebase. Domain-Specific Languages (DSLs) offer a more tailored approach, but come with limitations and development overhead. This paper introduces the Open Customizable Modeling Language (OpenCML), a universal DAG modeling language specification that aims to provide a standardized and customizable framework for modeling and scripting DAGs. Evaluations demonstrate that OpenCML offers expressive power, customizability, and interoperability, simplifying the learning process and providing a powerful solution for DAG modeling and scripting.

关键词： Optical fibers Directed acyclic graph Computational modeling US Department of Transportation Syntactics Extensibility Complexity theory DSL Photonics Domain specific languages

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