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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Pandey, Trilok Nath Pattanayak, Binod Kumar Tripathy, Abinash Chandrade, Utpal School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

A microgrid is a generating source used to decentralise the centralised power supply in a small community. It produces, distributes, and regulates the community's power. It can balance the load demands of clients with fluctuating power needs since it is adaptable, dependable, and able to deliver uninterrupted electricity. The utility grid is connected to smart Microgrids, which assist the powering industries' requirement for electricity. They rely heavily on contemporary, integrated information and communication technologies (such as edge computing, cloud computing, fog computing, IoT sensors, etc.). Despite the low cost of the services offered by cloud computing, response, processing, and resource management times are issues. As a supplement to cloud computing, edge computing develops, offering services and tackling issues like latency, security, and traffic. The edge servers are used by smart meters to offload their computational tasks. The energy usage is rising as more data is processed in the edge servers. In order to reduce energy usage, this article suggests optimized task scheduling to edge servers using human urbanization optimization algorithm (HUOA). © 2023 IEEE.

关键词： Edge computing Energy optimization Human urbanization optimization Smart grid

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Ultra-Wideband Based Self-Organizing Network Approach for Positioning Systems 2

Ultra-Wideband Based Self-Organizing Network Approach for Po...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Wang, Binfei Zhang, Deyu Zhou, Yue Wang, Yue Wang, Xiaoqing Shenyang Ligong University School of Information Science and Engineering Shenyang China Anhui University of Technology School of Computer Science and Technology Maanshan China

ISBN: (纸本)9798350368888

Aiming at the problem where the deployment of multiple positioning systems based on Ultra-Wideband (UWB) technology is too close to each other, leading to mutual interference, we propose a self-organizing network algorithm based on the UWB positioning system. The algorithm requires that when a certain tag appears within the communication range of a base station, the main base station randomly generates a Personal Area Network ID and utilizes the network identifier to form a LAN for all modules within its communication range. Once the networking is completed, there will be no more communication between different systems. Experiments have shown that the localization system utilizing this method can effectively solve the interaction problem between different systems. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-WAVE MIXING AND MICRO-CRACKS IN PIPES 18

NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-W...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Tan, Miaohong Sun, Maoxun Liu, Hongye Chen, Xiaorong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9798331523732

Pipes are widely used in various industries but are subject to plastic deformation and corrosion, during which microcracks may appear. Ultrasonic guided wave mixing has gained attention due to its high sensitivity. This study employs phase inversion and two-dimensional Fourier transform to investigate the nonlinear interaction between counter-propagating guided waves and microcracks. The generated sum-frequency components and difference-frequency components contain multiple flexural modes and propagate toward both ends of the pipeline. The generated sum-frequency components and difference-frequency components are used to locate the microcracks. The circumferential location of the microcracks is determined by polar diagrams (i.e., the amplitudes of difference-frequency or sum-frequency components and angles), while the axial location is determined by the relationship between the amplitudes of difference-frequency components or sum-frequency components and distance. © 2024 IEEE.

关键词： Pipeline corrosion

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Deep Learning-based Plant Leaf Disease Detection 7

Deep Learning-based Plant Leaf Disease Detection

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Shivaprasad, Kelothu Wadhawan, Ankita Lovely Professional University School of Computer Science Engineering Phagwara India Lovely Professional University Department of Computer Science Engineering Phagwara India

ISBN: (纸本)9798350397253

In this study, three deep learning models (CNN, VGG16, and VGG19) were compared for the objective was to detect plant diseases, and to accomplish this, a dataset comprising 9,127 images of plants annotated with disease labels was used to train and evaluate the model's using accuracy, F1 score, recall, and precision as performance metrics. The results show that CNN achieved the highest overall performance in the classification task, with an accuracy of 0.97 and an F1 score of 0.95. However, the VGG16 and VGG19 models also demonstrated strong performance, with accuracies of 0.96 and 0.95, respectively. The use of deep learning for plant illness interpretation has many advantages, such as automatically extracting relevant features from the input images and scalability. However, there are also limitations such as overfitting, computational resources, and the need for high-quality annotated data. Additional investigation is necessary to assess the efficacy of deep learning models under different conditions and to develop methods for addressing these *** research discusses the importance of evaluating models using multiple performance metrics to gain a comprehensive understanding of their capabilities. Additionally, it highlights potential areas for future research, including the use of transfer learning techniques, the evaluation of different deep learning architectures, and the use of additional performance metrics. The research further discusses the practical applications of deep learning models in image classification tasks, with a focus on plant leaf disease detection. The results of this research have the capacity to enhance the efficiency and efficacy of identifying and treating diseases in the agricultural sector. In general, this comparative analysis offers valuable perspectives on the functionality of deep learning models for image classification duties, with implications that extend to various sectors. © 2023 IEEE.

关键词： Convolutional neural networks

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

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computer Systems science & engineering 2023年第9期46卷 3371-3386页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few *** wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)*** this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)*** presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter *** the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)*** optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct *** comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

关键词： Weather wind speed predictive model chicken swarm optimization hybrid deep learning

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Short-Time Prediction Analysis Algorithm of Traffic Flow in Transportation Hubs Based on Cell Phone Signaling Data

Short-Time Prediction Analysis Algorithm of Traffic Flow in ...

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作者： Zhou, Hui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

With the rapid development of the mobile Internet, people's demand for transportation methods is also increasing. At the same time, road congestion is becoming more and more serious due to the wide availability and speed of access to information. How to improve the capacity and efficiency of the road network and reduce the traffic accident rate has become one of the important issues that the traffic management department needs to solve. In this paper, the traffic information in transportation hubs is analyzed based on the characteristics of mobile communication channels, multipath repetition coefficient, time series noise and other related factors based on short-term weather. Based on the short-term prediction analysis algorithm of mobile phone signaling data, this paper accurately speculates on the characteristics of traffic flow by constructing a model combining predicted flow and historical track under different weather conditions, and uses the short-term prediction results to control traffic flow, so as to ensure the safe and efficient operation of the transportation system in the transportation hub. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile telecommunication systems

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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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science China(Information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of Cyber Engineering Xidian University School of Computer Science and Technology Xidian University Department of Computer and Information Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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Comparative Study on Cassava Leaf Disease Classification 2

Comparative Study on Cassava Leaf Disease Classification

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mohanraj, G. Prabakaran, K. Rajkumar, M. School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

Manihot esculenta, the scientific name for cassava, is an important staple crop that feeds millions of people in tropical countries and provides a substantial amount of carbohydrates. To minimize these losses and guarantee food security, it is essential to identify plant illnesses as early as possible. Using innovative technology in image processing and machine learning, this comparative study explores the field of classifying illnesses of cassava leaves. The need for efficient and mechanized disease detection methods is pressing, and it might have a significant impact on food security and agricultural sustainability. our is the driving force behind our research. Code examples for processing data and machine learning model implementation are included in the research, together with the compilation and labelling of an extensive dataset. The findings and outcomes from the tests on disease classification are carefully explained. Accompanying them is a comprehensive examination of the assessment metrics, encompassing ROC curves, cross-validation outcomes, recall, accuracy, and a F1 score. These results not only highlight the effectiveness of the suggested methodology but also provide a comparative evaluation of various models and techniques. The study's comparison assessment and investigation findings highlight the possibility for more precise and scalable illness detection methods, providing a foundation for further research in this area. © 2024 IEEE.

关键词： Food supply

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