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Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

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Journal of Harbin Institute of technology(New Series) 2024年第4期31卷 52-60页

作者： J Srinivasan S Uma Saleem Raja Abdul Samad Jayabrabu Ramakrishnan Department of Computer Applications Madanapalle Institute of Technology&Science(MITS)Madanapalle 517325Andhra PradeshIndia Department of Information Technology Panimalar Engineering CollegeChennai 600123India Information Technology Department College of Computing and Information SciencesUniversity of Technology and Applied Sciences-Shinas 324Sultanate of Oman Department of Information Technology and Security College of Computer Science and Information TechnologyJazan UniversityJazan 45142Kingdom of Saudi Arabia

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over *** the original images may provide limited insights,they harbor considerable potential for identifying clusters and *** aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural *** a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific *** findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature *** study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

关键词： spatial pattern analysis EMD ACO

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Enhancing Deepfake Detection:Proactive Forensics Techniques Using Digital Watermarking

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computers, Materials & Continua 2025年第1期82卷 73-102页

作者： Zhimao Lai Saad Arif Cong Feng Guangjun Liao Chuntao Wang School of Automation Guangdong University and TechnologyGuangzhou510006China School of Immigration Administration(Guangzhou) China People’s Police UniversityGuangzhou510663China Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Cyber Police Division Guangzhou Public Security BureauGuangzhou510030China School of Forensic Science and Technology Guangdong Police CollegeGuangzhou510320China College of Mathematics and Informatics South China Agricultural UniversityGuangzhou510642China

With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly *** paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking *** detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic *** contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake *** offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal ***,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking *** systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.

关键词： Deepfake proactive forensics digital watermarking traceability detection techniques

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Intelligent System for Crime and Insecurity Management 2

Intelligent System for Crime and Insecurity Management

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Nwankwo, Wilson Ukaoha, Kingsley C. Irikefe, Eti Friday Peter, Ovilli Oghorodi, Duke Jeroh, Esemena Eterighoi, Okpu Okpomo Atajeromavwo, Edafe John Adebowale, Oyewale Mojeed Ogheneochuko, Ubrurhe Delta State University of Science and Technology Department of Cyber Security Ozoro Nigeria Pan Atlantic University Department of Computer Science Lekki Nigeria Delta State University of Science and Technology Department of Information Systems and Technology Ozoro Nigeria Delta State University of Science and Technology Dept. of Information Technology Ozoro Nigeria Delta State University of Science and Technology Department of Computer Science Ozoro Nigeria Delta State University of Science and Technology Department of Library and Information Science Ozoro Nigeria Delta State University of Science and Technology Dept. of Cyber Security Ozoro Nigeria Delta State University of Science and Technology Department of Software Engining Ozoro Nigeria

ISBN: (纸本)9798350358834

As the global law enforcement workforce faces unprecedented challenges such as declining numbers and shifting demographics, the need for innovative solutions to manage crime and insecurity is becoming increasingly critical. This paper presents a multifaceted approach aimed at harnessing the power of intelligent systems to address the complex and evolving landscape of crime in societies with diminishing Police workforce in Nigeria. The study provides a comprehensive overview of the demographic shifts leading to a declining workforce and their far-reaching implications on crime rates, public safety, and law enforcement resources. It underscores the urgency of adapting to this new reality and the need for intelligent systems to assist in this endeavor. The paper adopts the object-oriented methodology in the design and development of an intelligent system tailored for crime management and insecurity mitigation. Drawing from the fields of artificial intelligence, machine learning, and data analytics, this system employs cutting-edge techniques for predictive policing, resource allocation optimization, and risk assessment. It integrates data from various sources, including crime statistics, demographic data, economic indicators, and social media sentiment analysis, to provide a holistic understanding of the crime landscape. Furthermore, this paper emphasizes the ethical considerations and safeguards built into the intelligent system to prevent bias, discrimination, and privacy infringements. It discusses the importance of transparency, accountability, and community engagement in the deployment and operation of such systems. The study also highlights real-world case studies and pilot implementations of the intelligent system in select regions facing workforce decline. It showcases how the system has contributed to more efficient law enforcement practices, reduced response times, and enhanced crime prevention strategies. The paper outlines a roadmap for future research, emp

关键词： Crime

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Predictive Modeling of Poultry Growth Optimization in IoT-Connected Farms Using SVM for Sustainable Farming Practices 10

Predictive Modeling of Poultry Growth Optimization in IoT-Co...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kiruthika, R. Sahoo, Santosh Kumar Vathani, B. Santha Reddy, G. Rakesh Rajanarayanan, S. Prakash, S. Sri Sairam Institute of Technology Department of Chemistry Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Sphoorthy Engineering College Department of CSE-Cyber Security Telangana Hyderabad India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Modern agriculture uses the Internet of Things (IoT) to improve production and sustainability. This research predicts poultry growth on IoT-connected farms to enhance sustainable farming. Predictive models are created using Support Vector Machines (SVM), a machine learning approach, and poultry farm IoT sensor data. Temperature, humidity, feeding schedules, and disease prevalence are examined to improve poultry development. SVM models are trained and tested using historical poultry growth data and real-time IoT sensor data. The models forecast poultry growth and find abnormalities. The research uses SVM to help farmers make data-driven choices to boost poultry growth and avoid resource waste. It shows that SVM-based predictive modeling optimizes poultry growth in IoT-connected farms. These models improve poultry growth management, mortality rates, and resource use, promoting sustainable farming. Agriculture will be transformed by IoT and machine intelligence, enabling efficient and sustainable farming. This work advances precision agriculture in poultry, where data-driven decision-making and IoT connection are crucial to sustainable farming. The system can reach a 90% success rate in predicting poultry growth via real-time data analysis, which allows farmers to enhance their management techniques and environmental controls for sustainable farming. © 2024 IEEE.

关键词： Anomaly detection

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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GreenTrust: Trust Assessment Using Ensemble Learning in Internet of Microgrid Things

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IEEE Internet of Things Journal 2024年

作者： Ali, Wajahat Din, Ikram Ud Almogren, Ahmad Rodrigues, Joel J. P. C. University of Haripur Department of Information Technology Pakistan King Saud University College of Computer and Information Sciences Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia

With the rise in industries and population, electricity demand is increasing daily. Microgrids play a crucial role in providing green energy by utilizing renewable energy resources. Microgrids not only help meet the growing electricity demand but also reduce global warming and greenhouse effects. However, many homeowners are hesitant or reluctant to share their excess energy resources with other Microgrid or traditional electric grid users. In this paper, we propose a hybrid deep learning and machine learning stacking model named GreenTrust. GreenTrust consists of three evaluation deep learning models at the base level and a single machine learning model at the meta level. GreenTrust first establishes trust among home users using trust parameters. Once trust is buildup, a Microgrid can share its resources with other grid users. Results show that the hybrid model outperforms than other stand alone machine learning schemes, such as Random Forest, XGBoost, and AdaBoost, in terms of accuracy, precision, recall, and F1 score. © 2014 IEEE.

关键词： Adversarial machine learning

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Evaluation and Optimization of computer Multimedia Assisted Network Teaching Information Ecosystem 24

Evaluation and Optimization of Computer Multimedia Assisted ...

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24th International Arab Conference on Information technology, ACIT 2023

作者： Tarish, Hiba A. Hassan, Rosilah Jaber, Mustafa Musa Raheema, Alaa Q. University of Technology Civil Engineering Department Baghdad Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Iraqi Commission ForComputers and Informatic Baghdad Iraq

ISBN: (纸本)9798350384307

At present, the continuous progress in contemporary multimedia innovation on optimization of computer-assisted network teaching with the evaluation of digital learning assets improves students in their cognitive structures by including diverse media aspects that promote learning and memory. The significant challenges in digital multimedia aided information education are corrective maintenance in classroom instructors on the use of audio-visual, poor quality of visuals, and applications that can suddenly lapse into a date. This paper introduces technology in computer Multimedia-assisted Network Teaching based on Big Data (CMNT-BD) network that considerably enhances the accuracy of the interactive development model. BD-based teaching modes can significantly influence student learning, according to parametric studies. In addition to presenting essential knowledge to learners' better-suited forms of learning, it allows for individualized educational experiences. A design technique for a digital learning system is provided in the theoretical analysis of the education platform with the attributes and purposes of the IoT in multimedia technology. This study compares the Internet of Things (IoT) online course on multimedia technologies to a standard online class as a final step. Analytic teaching resources can be effectively connected and communicated using this strategy, resulting in greater integration and a greater connection between the various teaching materials. This conclusion proposes corresponding suggestions to optimize the teaching information ecosystem in the multimedia environment. © 2023 IEEE.

关键词： Ecosystem Effectiveness Environment Multimedia Network Teaching

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GAI-Enhanced Intrusion Detection using Began-GRU Framework to Address Data Imbalance

GAI-Enhanced Intrusion Detection using Began-GRU Framework t...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Shivanthana S Ciza Thomas Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

With the developing communication technologies, the networks become more complicated and data is raised, which expands the surface of threats and increases the security risks. However, due to evolution of modern network environments vulnerability analysis is a difficult task and traditional Network Intrusion Detection Systems have problems in detection of advanced and new threats. One Cardinal restriction is the data imbalance: normal traffic mete out malicious traffic to such extent that machine learning models are not capable of predicting less observed classes of attack This work proposes a new intrusion detection model by integrating Boundary Equilibrium Generative Adversarial Networks with Gated Recurrent Units to help solve this problem. The integration of generative methods to invent images that mimic real images and temporal sequence methods enhances the quality and balance of training images. It was discovered that this new approach significantly increased the classification accuracy with regard to the detection of some specific attack methods, thereby enhancing the performance of the intrusion detection system.

关键词： Training Renewable energy sources Network intrusion detection Machine learning Predictive models Logic gates Generative adversarial networks Data models Communications technology security

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Optimized Data Storage Algorithm of IoT Based on Cloud Computing in Distributed System

Optimized Data Storage Algorithm of IoT Based on Cloud Compu...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Deepthi, K.Jaya Balakrishnan, T.Suresh Krishnan, Prabhakar Ebenezar, U.Samson Nageshwari Mohan Babu University Department of Aiml AP Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy620009 India

ISBN: (纸本)9798350373783

IoT and cloud technology are vital for large systems. An improved data storage technique for IoT applications and cloud distribution is described in this research. Scalability, reliability, latency, and resource usage are internal and external to the algorithm. The approach above scales well with work portion or dataset size. The first experiment shows the algorithm scaling perfectly even as IoT devices generate additional data. Experiment 2 proves linear scalability works for 100,000 IoT devices, while Experiment 3 efficiently organizes 1 million data points. It proved the algorithm by tolerating manufactured faults. To ensure data service availability, Experiment 1 tests the system's operational dependability on node failures and network disturbances. With 0.1% downtime even under challenging conditions, experiment 2 showed the algorithm's incredible robustness in real-world applications. To improve data organization, this experiment exhibits data consistency when nodes fail. Lower and constant time improves operation safety and reliability. Algorithm 1 shows how the algorithm can quickly answer data queries and keep up with the massive amount of data transfers, supporting low-latency IoT applications. Average data retrieval latency of experiment 2 is 10 ms, which is real-time for vital information. Trial 3 processes data online with a latency © 2024 IEEE.

关键词： Cloud storage

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