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Classification of Crop Across Heterogeneous Landscape Through Experienced Artificial Bee Colony

SN computer science 2024年第4期5卷 428页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Yogeesh, G.H. Dileep, R. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan India Department of Electronics and Communication Engineering GSSS Institute of Engineering and Technology for Women Karnataka Mysore India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan India

This study aims to develop an experienced artificial bee colony algorithm which is proposed for classification of crops using remote sensed data. The study was conducted in Devnur village located in Nanajangudu taluk of Mysore district, where a highly differed irrigated agriculture system is present. The study is designed to overcome the issues of misclassification in crop patterns due to similar crop Phenology. In Artificial Bee Colony algorithm a single parameter like waggle dance is used for training and validating the classes particularly in high resolution images, this may lead to misclassification of classes like crops which has a same spectral features. In order to avoid this misclassification, a group of experienced employed bee is been trained and updated by each employed bee through a waggle dance where each employed bee depends on its own experience and the current terrain in each space. Here these features help in identifying the classes and updating of the weights in order to map the agents and identifying the classes and robust the spatial and spectral features. This resultant algorithm is called Experienced Artificial bee colony (EABC). The proposed strategy gives a structure within which a pixel based EABC gives commonly integral data to one another, so characterization process is refined through waggle dance. The findings of the experiment indicate that the suggested approach exhibits a 7% and 6% enhancement in Level 2, and an 8% and 5% improvement in Level 3 categories, respectively, compared to the Artificial Bee Colony (ABC) algorithm and Support Vector Machine (SVM). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ABC Cropped area EABC LISS IV data SVM

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and communications in Medicine (DICOM)

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A Fractional Graph Fourier Transform Method for Missing Data Recovery of Sensor Network 13

A Fractional Graph Fourier Transform Method for Missing Data...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350355079

The utilization of sensor networks for collecting climate-related data, encompassing temperature, rainfall, pressure, and wind measurements, has become widespread in endeavors to understand climate change. Typically, these data are depicted through an irregular graph model, where each sensor corresponds to a vertex, and edges denote similarity relations between sensors, with the measured data at each vertex represented as graph signals. However, a notable challenge in sensor networks stems from potential data gaps due to sensor malfunctions or communication failures, necessitating the development of methods to accurately estimate missing signal values. This paper addresses this challenge by introducing a method for recovering missing data. Initially, it defines the fractional graph Fourier transform (FrGFT) of irregular data and utilizes the bandlimitedness property of FrGFT to devise a missing data recovery approach. Subsequently, it investigates the relationship between recovery error and fractional order to pinpoint the optimal fractional order that minimizes recovery error. Finally, the efficacy of the proposed FrGFT method is demonstrated using temperature data collected by a sensor network in the USA. Additionally, a performance comparison with the conventional graph Fourier transform (GFT) method is conducted to highlight the superiority of the proposed approach. © 2024 IEEE.

关键词： graph Fourier transform graph signal processing missing data recovery sensor network

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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Music genre classification and recognition using convolutional neural network

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Multimedia Tools and Applications 2025年第4期84卷 1845-1860页

作者： Narkhede, Nandkishor Mathur, Sumit Bhaskar, Anand Kalla, Mukesh Shah and Anchor Kutchhi Engineering College Mumbai Chembur India Department of Electronics and Communication Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science Engineering Sir Padampat Singhania University Rajasthan Udaipur India

The music genre classification system is crucial to users in the digital music business since it allows them to be more effective. Music suggestion and availability to consumers is one of the most successful uses of genre classification. Songs may be easily accessible by users when the genre of the song is recognized, and music recommendations to users are made simple with an accurate categorization system in place. Furthermore, automated genre categorization is necessary to tackle difficulties such as finding similar songs, identifying cultures that would enjoy certain music, and conducting surveys. Machine learning approaches have recently been shown to be useful in a variety of classification tasks, including music genre categorization. As a result, this research investigates the use of Convolutional Neural Networks (CNN) for music genre categorization. For this study, a fresh dataset of 1000 traditional music from ten genres was employed. Content-based features, were retrieved from the songs in the dataset and used as input into the classifier, as feature extraction is critical to audio analysis. We got the results of the accuracy level of the system is 98.9% with a precision of 98.7%, recall of 98.5%, and f1 score of 97.5%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Power spectrum

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An Energy Efficient IoT Based Smart Street Lighting Using Low Cost SOC 1

An Energy Efficient IoT Based Smart Street Lighting Using Lo...

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1st IEEE International Conference on Electronics, Computing, communication and Control Technology, ICECCC 2024

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Fake News Detection Based on Cross-Modal Ambiguity Computation and Multi-Scale Feature Fusion

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computers, Materials & Continua 2025年第5期83卷 2659-2675页

作者： Jianxiang Cao Jinyang Wu Wenqian Shang Chunhua Wang Kang Song Tong Yi Jiajun Cai Haibin Zhu State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China School of Computer Science and Engineering Guangxi Normal UniversityGuilin541004China Department of Computer Science Nipissing UniversityNorth BayON P1B 8L7Canada

With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant *** social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant *** solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)***,the model deeply explores the textual content,image content,and frequency domain ***,it employs a Co-Attention mechanism for cross-modal ***,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news *** verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline *** improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.

关键词： Fake news detection multimodal cross-modal ambiguity computation multi-scale feature fusion

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