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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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LMCF-FS: A Novel Lightweight Malware Classification Framework Driven by Feature Selection 6th

LMCF-FS: A Novel Lightweight Malware Classification Framewor...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yun, Cui Zhou, Lei Xing, Shuangshuang Yang, Ning Zhao, Pan Chen, Zhiguo School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and School of Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

The rapid increase in the number of malware and its variants poses a significant threat to internet security. While existing machine learning-based malware classification methods can improve accuracy, they often require complex feature engineering. In contrast, converting executable files into images and classifying them using deep learning models can reduce the reliance on prior knowledge of malware features and simplify the feature processing. However, most studies have not adequately considered the impact of image pixel features and size on classification results. In addition, the model structure of deep learning classification methods based on pre-trained parameters and transfer learning is usually more complex, resulting in the classification model requiring more memory and time overhead. To solve the above problems, this paper proposes a novel lightweight malware classification framework LMCF-FS driven by feature selection. This framework uses a proposed new feature selection method based on pixel pair features to improve feature sparsity and optimize image texture features, thereby improving feature expression capabilities. At the same time, an interpolation algorithm is used to balance the image size. In addition, a new lightweight malware classification model ConvInceptionNet is built based on the Inception-C module to balance accuracy, computational cost, and number of parameters, thereby improving malware classification efficiency. Our proposed LMCF-FS framework achieves a classification accuracy of 99.12% on the Microsoft Malware Classification Challenge Dataset (BIG2015 Dataset), and it only takes 0.92 ms to predict a 224 × 224 malware image, verifying LMCF-FS effectiveness in dealing with malware classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image texture

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Multi-dimensional Classification via Selective Feature Augmentation

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Machine Intelligence Research 2022年第1期19卷 38-51页

作者： Bin-Bin Jia Min-Ling Zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented *** this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented ***, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

关键词： Machine learning multi-dimensional classification feature augmentation feature selection class dependencies

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A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

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International Journal of information technology (Singapore) 2024年第3期16卷 1801-1814页

作者： Holla, M. Raviraja Suma, D. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India

Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of 112 × for 256 × 256 color images. © 2024, The Author(s).

关键词： Contrast GPU Multi-secret Real-time Security

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Two-dimension high efficiency beam steering based on liquid crystal polarization gratings

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Optics Express 2025年第10期33卷 21425-21440页

作者： Li, Zifan Yu, Bijun Gong, Junhao Liu, Yutong Fan, Lina Gao, Xin Yin, Jiahang Ding, Tongshan Zhang, Dawei Wang, Jun Hong, Ruijin Mao, Hongmin Lu, Huanjun Cao, Zhaoliang Key Laboratory of Intelligent Optoelectronic Devices and Chips of Jiangsu Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Advanced Technology Research Institute of Taihu Photon Center School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electronics and Information Engineering Suzhou University of Science and Technology Jiangsu Suzhou215009 China

Cascaded liquid crystal polarization grating (LCPG) structure is commonly used to realize two-dimension large angle optical beam steering. In this architecture, different LCPGs have different incident angles. As a liquid crystal half-wave plate (LC HWP) is placed in front of the LCPG to control steering direction, the different incident angles will affect the phase modulation of LC HWP and then, decrease the steering efficiency. In this paper, the steering efficiency of cascaded LCPGs under tilt incidence is considered and a compensation method is proposed to increase the steering efficiency. A simple analysis model is established to calculate the steering efficiency of cascaded LCPG structure for tilt incidence with arbitrary direction. Further, the effect of tilt incidence on the phase modulation of LC HWP is compensated and a simple formula is given to compute the compensation voltage. A validation experiment is performed to verify the proposed method. All the simulated and experimental results indicate that the steering efficiency of cascaded LCPGs can be improved for two-dimension beam scanning. This work is helpful for high efficiency beam steering based on cascaded LCPGs. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Bragg gratings

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Optimizing accuracy and efficiency in real-time people counting with cascaded object detection

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International Journal of information technology (Singapore) 2024年 1-14页

作者： Holla, M. Raviraja Suma, D. Holla, M. Darshan Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India

Growing concerns about public safety have driven the demand for real-time surveillance, particularly in monitoring systems like people counters. Traditional methods heavily reliant on facial detection face challenges due to the complex nature of facial features. This paper presents an innovative people counting system known for its robustness, utilizing holistic bodily characteristics for improved detection and tallying. This system achieves exceptional performance through advanced computer vision techniques, with a flawless accuracy and precision rate of 100% under ideal conditions. Even in challenging visual conditions, it maintains an impressive overall accuracy of 98.42% and a precision of 97.51%. Comprehensive analyses, including violin plot and heatmaps, support this outstanding performance. Additionally, by assessing accuracy and execution time concerning the number of cascading stages, we highlight the significant advantages of our approach. Experimentation with the TUD-Pedestrian dataset demonstrates an accuracy of 94.2%. Evaluation using the UCFCC dataset further proves the effectiveness of our approach in handling diverse scenarios, showcasing its robustness in real-world crowd counting applications. Compared to benchmark approaches, our proposed system demonstrates real-time precision and efficiency. © The Author(s) 2024.

关键词： Cascaded detector computer vision Object detection People counting Real-time

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Video Recommendation System Using Machine-Learning Techniques

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Journal of Harbin Institute of technology(New Series) 2024年第4期31卷 24-33页

作者： Meesala Sravani Ch Vidyadhari S Anjali Devi Department of Computer Science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information Technology Gokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through *** this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different *** expectation depends on past evaluations,history,interest,IMDB rating,and so *** can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific *** required datasets for the video are taken from *** recommender framework is executed by utilizing Python Programming *** building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the *** that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information *** last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

关键词： video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence

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An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

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IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

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An Energy-Efficient Solution for Improving the Flooding Time Synchronization Protocol in Wireless Sensor Networks 5

An Energy-Efficient Solution for Improving the Flooding Time...

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5th International Conference on computer Communication and the Internet, ICCCI 2023

作者： Yang, Jia-Zhang Chang, Chia-Chi Wei, Ching-Chuan Chaoyang University of Technology Department of Information and Communication Engineering Taichung Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan

ISBN: (纸本)9798350326956

In this paper, we use a time synchronization technique called Flooding Time Synchronization Protocol (FTSP), which enables all nodes in the WSNs to operate in a highly precise environment, reducing clock offset caused by long-term operation. However, the frequent execution of time synchronization techniques may lead to excessive power consumption, ultimately resulting in system failure due to limited energy resources. To address these issues, we designed a system architecture and employed the DS1342 as a replacement for the internal sleep timer of the CC2530 microcontroller, aiming to reduce the overall system energy consumption. We employed the low-power ZigBee wireless communication protocol for transmitting the time synchronization packets among the nodes and measured the power consumption between sleep and active modes at specific time intervals. The experimental results demonstrated that the FTSP scheme successfully improved the time accuracy of the nodes and prevented clock offset from expanding outward. Additionally, our proposed system architecture and approach ensured ultra-low power consumption while running the time synchronization technique. © 2023 IEEE.

关键词： Electric power utilization

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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