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A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

computers, Materials & Continua 2022年第10期73卷 343-361页

作者： Sankar Sennan Digvijay Pandey Youseef Alotaibi Saleh Alghamdi Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Technical Education Department of Electronics EngineeringInstitute of Engineering and TechnologyDr.A.P.J.Abdul Kalam Technical UniversityLucknowIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In the present scenario,Deep Learning(DL)is one of the most popular research algorithms to increase the accuracy of data *** to intra-class differences and inter-class variation,image classification is one of the most difficult jobs in image *** or spinach recognition or classification is one of the deep learning applications through its *** is more critical for human skin,bone,and hair,*** provides vitamins,iron,minerals,and *** is beneficial for diet and is readily available in people’s *** researchers have proposed various machine learning and deep learning algorithms to classify plant images more accurately in recent *** paper presents a novel Convolutional Neural Network(CNN)to recognize spinach more *** proposed CNN architecture classifies the spinach category,namely Amaranth leaves,Black nightshade,Curry leaves,and Drumstick *** dataset contains 400 images with four classes,and each type has 100 *** images were captured from the agricultural land located at Thirumanur,Salem district,Tamil *** proposed CNN achieves 97.5%classification *** addition,the performance of the proposed CNN is compared with Support Vector Machine(SVM),Random Forest,Visual Geometry Group 16(VGG16),Visual Geometry Group 19(VGG19)and Residual Network 50(ResNet50).The proposed provides superior performance than other models,namely SVM,Random Forest,VGG16,VGG19 and ResNet50.

关键词： Accuracy convolutional deep learning plant neural networks spinach

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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EACR-LEACH:Energy-Aware Cluster-based Routing Protocol for WSN Based IoT

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computers, Materials & Continua 2022年第8期72卷 2159-2174页

作者： Sankar Sennan Kirubasri Youseef Alotaibi Digvijay Pandey Saleh Alghamdi Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Technical Education Department of Electronics EngineeringInstitute of Engineering and TechnologyDr.A.P.J.Abdul Kalam Technical UniversityLucknowIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Internet of Things(IoT)is a recent paradigm to improve human ***,number devices are connected to the Internet ***,the people can control and monitor the physical things in real-time without *** IoT plays a vital role in all kind of fields in our world such as agriculture,livestock,transport,and healthcare,grid system,connected home,elderly people carrying system,cypher physical system,retail,and intelligent *** IoT energy conservation is a challenging task,as the devices are made up of low-cost and low-power sensing devices and local *** networks have significant challenges in two areas:network lifespan and energy ***,the clustering is a right choice to prolong the energy in the *** LEACH clustering protocol,sometimes the same node acts as CH again and again *** overcome these issues,this paper proposes the Energy-Aware Cluster-based Routing(EACRLEACH)protocol in WSN based *** Cluster Head(CH)selection is a crucial task in clustering protocol inWSN based *** EACR-LEACH,the CH is selected by using the routing metrics,Residual Energy(RER),Number of Neighbors(NoN),Distance between Sensor Node and Sink(Distance)and Number of Time Node Act as CH(NTNACH).An extensive simulation is conducted on MATLAB *** accomplishment of EACR-LEACH is compared to LEACH and *** proposed EACR-LEACH protocol extends the network’s lifetime by 4%-8%and boosts throughput by 16%–24%.

关键词： Cluster head clustering protocol internet of things routing metrics wireless sensor networks

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Fractal dimension analysis for improved decision-making in non-stationary multi-arm bandit problems: a machine learning approach

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The European Physical Journal Special Topics 2025年 1-16页

作者： Nandhini, S. Kala Pandian, J. Arun Department of Computer Applications School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

In this research, we propose a novel approach to addressing the exploration–exploitation dilemma in multi-armed bandit (MAB) algorithms using fractal dimensions. The fractal dimension is used in the algorithms to represent the reward distributions of arms which represents the uncertainty of the arm in receiving the reward. The fractal dimension of the reward distribution is implemented in the most popular MAB optimization algorithms, such as Epsilon-Greedy, Upper Confidence Bound (UCB), Exponential-weight algorithm for Exploration and Exploitation (EXP3), and Thompson Sampling in this study. The algorithm prefers to choose arm with the least fractal dimension, as a lower fractal dimension represents less uncertainty of the arm. The performance of the fractal-enhanced MAB optimization algorithms is compared with traditional algorithms in non-stationary environments with various numbers of arms. The proposed approach provides a novel way to quantify and utilize the uncertainty of each arm in MAB problems.

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Lip Reading with 3D Convolutional and Bidirectional LSTM Networks on the GRID Corpus 2

Lip Reading with 3D Convolutional and Bidirectional LSTM Net...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Prashanth, B.S. Manoj Kumar, M.V. Puneetha, B.H. Lohith, R. Darshan Gowda, V. Chandan, V. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (纸本)9798350372892

In recent years, the application of artificial intelligence has revolutionized the field of lip reading by enabling the development of sophisticated models capable of accurately interpreting lip movements from video data. This work presents a novel deep learning approach to lip reading, focused on decoding spoken text from video sequences of lip movements. Traditional lip reading methods involve separate stages for visual feature design and prediction. The proposed system utilizes an endto-end deep learning model to directly map video frames to text transcriptions, leveraging 3D convolutional neural networks and bidirectional Long Short-Term Memory. By analyzing visual cues from lipmotions, the system can interpret speech, improving accessibility for individuals with hearing impairments and enabling communication in noisy environments. Compared to existing lip reading techniques, the deep learning model achieves superior performance on benchmark datasets, demonstrating its effectiveness in this challenging task. The model has achieved a character error rate of 1.54% and a word error rate of 7.96% on the best model. © 2024 IEEE.

关键词： Convolutional neural networks

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter 18

Comparative Analysis of Word Embedding and Machine Learning ...

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18th Conference on computer Science and Intelligence systems, FedCSIS 2023

作者： Akshar, Tumu Kumar, Lov Yogita Murthy, Lalita Bhanu Department of Computer Science & Information Systems BITS Pilani Hyderabad Campus India National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9788396744784

In recent times, software developers widely use instant messaging and collaboration platforms, as these plat-forms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevs-rest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prpediction ability. © 2023 Polish information Processing Society.

关键词： Classification (of information)

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on information Communication technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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