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Precision Agriculture System Using Big data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. ***, Research Scholar, department of data science and Business System, School of Computing, Srm Institute of science and technology,Ktr

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SSRN 2022年

作者： Vasudevan, N. Karthick, T. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Chennai India

The agricultural sector is one of India's most important and major endeavors, and it is also critical to the country's economic development. Agriculture is one of the most important things that contributes to humanity's pleasure and welfare, as well as serving as a deterministic element in a nation's economic well-being. Smart agriculture, or the application of information technology in agriculture, attempts to provide precise disease management, irrigation and yield prediction in crop production. We have implemented Big data Analytics and Internet of Things in the agricultural field, which is nothing but Precision Agriculture. This will extensively impact on the crop yield positively. Precision agriculture is utilizing IoT and Big data to enhance crop quality, therefore increasing total output rates. In this paper we have implemented the N stage – CNN (Convolutional Neural Networks) using the Smart Agriculture dataset, using parameters such as temperature, humidity, rainfall, pH and solar irradiance. In this paper, irrigation scheduling is performed using machine learning techniques and plant disease detection using DoubleGAN method. As we have taken into consideration almost all required parameters for plant growth with irrigation scheduling and plant disease detection, the yield prediction is precise. The principle aim of this work is to come up with N stage CNN model that will increase the yield irrespective of the type of crop and thereby largely contributing to the agricultural sector, which will in turn increase food production in the coming years. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Berry-Esseen bounds for self-normalized sums of locally dependent random variables

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science China Mathematics 2024年第11期67卷 2629-2652页

作者： Zhuo-Song Zhang Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal *** this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment *** proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other *** main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.

关键词： Berry-Esseen bounds self-normalized sums local dependence m-dependence graph dependency

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A generalized integrated framework for urban public transport operations evaluation based on interval neutrosophic TODIM and EDAS technique

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Soft Computing 2024年第17-18期28卷 10331-10344页

作者： Cai, Yunpeng Department of Data Science and Big Data Technology Shenyang Normal University Liaoning Shenyang110034 China

With the acceleration of urbanization construction, the contradiction between supply and demand of urban public transportation resources is becoming increasingly prominent, resulting in increasingly serious problems such as traffic congestion and environmental pollution, which has a great impact on the sustainable development of cities. The experience of mature markets both domestically and internationally indicates that priority must be given to the development of public transportation networks with rail transit as the backbone. Due to the fact that the operation of the rail transit system should not only focus on market benefits but also meet social benefits as much as possible, evaluating efficiency has become a prerequisite for managing rail transit and an important topic in the performance evaluation of urban public transportation. The urban public transport operations evaluation is a multiple-attribute group decision-making (MAGDM). Then, the TODIM and EDAS method has been constructed to deal with MAGDM issues. The interval neutrosophic sets (INSs) are constructed as an effective tool for representing uncertain information during the urban public transport operations evaluation. In this manuscript, the interval neutrosophic number TODIM-EDAS (INN-TODIM-EDAS) method is constructed to solve the MAGDM under INSs. Finally, a numerical example study for urban public transport operations evaluation is constructed to validate the INN-TODIM-EDAS method. The main research contribution of this paper is constructed: (1) the INN-TODIM-EDAS method is put up for MAGDM with INSs;(2) the INN-TODIM-EDAS method is put up for urban public transport operations evaluation and were compared with existing methods;(3) Through the detailed comparison, it is evident that INN-TODIM-EDAS method for urban public transport operations evaluation proposed in this paper are effective. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traffic congestion

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Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

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International Journal of Speech technology 2024年第2期27卷 389-404页

作者： Khalil, Maysa Azzeh, Mohammad Data Science Department Princess Sumaya University for Technology Amman Jordan

Twitter is a powerful platform for communication and information sharing but is also susceptible to spreading false information. This false information has adverse consequences for society and can significantly impact public perception, decision-making, and political outcomes. Therefore, there is an urgent need to build a fake news detection system that can accurately catch false information before it is disseminated. Building such a system requires the existence of good quality and trustworthy labeled datasets. The limitations of the existing datasets are undeniable. Most of them are not updated to reflect the advanced generation patterns of the new fake news creators. Thanks to Truth Seeker research team, who offered a large-scale fake news dataset that was labeled based on Amazon Mechanical Turk. The dataset was collected between 2009 and 2022 and then validated according to a robust procedure to ensure its quality and reliability. However, the credibility and trustability of this dataset is still questionable. In this paper, we study and analyze the feasibility of building a fake news detection model based on deep learning using Truth seeker dataset. Mainly we investigated the impact of different text representation techniques on the accuracy of deep learning models. Also, we investigated the importance of hand-crafted features associated with the dataset in the final results. The results have shown that using truth seeker dataset show potential to help social media platforms in detecting fake news. on the other hand, using deep contextualized text representation produced more accurate results compared to word2vec and TF-IDF techniques. The impact of hand-crafted features on the final performance of deep learning models is often negligible, and it is suggested to be excluded from the final models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural Computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Arabic cyberbullying detection system using convolutional neural network and multi-head attention

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International Journal of Speech technology 2024年第3期27卷 521-537页

作者： Azzeh, Mohammad Alhijawi, Bushra Tabbaza, Abedrahman Alabboshi, Omar Hamdan, Nancy Jaser, Dareen Department of Data Science Princess Sumaya University for Technology Amman-Jordan Jordan

In the digital era, cyberbullying is a growing concern that impacts the well-being of its victims. The rise of cyberbullying among social media users necessitates robust detection solutions. One of these solutions is to build a classification model to identify the true cyberbullying messages. Most of these models have been designated for English and other Western languages. The Arabic language is still under-resourced and lacks research in the field, even though it is deemed one of the most widely spoken languages in the world. This study introduces a new Arabic cyberbullying detection method, namely AraCB. AraCB is a deep learning-based method that adopts convolutional neural networks, attention, and ResNet to detect cyberbullying text. The proposed method uses word2vec, the attention mechanism, and positional encoding to represent the semantic meaning, sequential nature of language, and cyberbullying contexts. Also, the local patterns in the word embeddings are captured using a convolutional layer. to accomplish that, we used one of the popular Arabic tweet dataset called ArCybC. Numerous experiments were conducted to evaluate and compare AraCB to popular classification models, employing different vector sizes, embedding methods, and aggregation techniques. On average, AraCB enhances accuracy, recall, precision, F1-score, and AUC by 16.5%, 29.89%, 18.4%, 26.93%, and 16.66%, respectively. In addition, ArCybC performance aligns closely with that of alternative methods employing transformer-based text representation approaches, all while avoiding the computational complexities often associated with such techniques. The results highlight the superiority of AraCB, emphasizing its robust and efficient cyberbullying detection, irrespective of the vector size. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Route selection for opportunity-sensing and prediction of waterlogging

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Frontiers of Computer science 2024年第4期18卷 147-159页

作者： Jingbin WANG Weijie ZHANG Zhiyong YU Fangwan HUANG Weiping ZHU Longbiao CHEN College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Department of Computer Science and Technology Xiamen UniversityXiamen 361005China

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.

关键词： waterlogging prediction sparse crowdsensing active learning route selection graph convolutional network

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An intelligent approach for autism spectrum disorder diagnosis and rehabilitation features identification

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Neural Computing and Applications 2024年第4期37卷 2557-2580页

作者： Ghnemat, Rawan Al-Madi, Nailah Awad, Mohammad Computer Science department Princess Sumaya University for Technology Amman Jordan Data Science department Princess Sumaya University for Technology Amman Jordan

Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recursive feature elimination (SVM-RFE) method in its approach for ASD classification using the phenotypic and Automated Anatomical Labeling (AAL) Brain Atlas datasets of the Autism Brain Imaging data Exchange preprocessed dataset. The functional connectivity matrix (FCM) is computed for the AAL data, generating 6670 features representing pair-wise brain region activity. The SVM-RFE feature selection method was applied five times to the FCM data, thus determining the optimal number of features to be 750 for the best performing support vector machine (SVM) model, corresponding to a dimensionality reduction of 88.76%. Pertinent phenotypic data features were manually selected and processed. Subsequently, five experiments were conducted, each representing a different combination of the features used for training and testing the linear SVM, deep neural networks, one-dimensional convolutional neural networks, and random forest machine learning models. These models are fine-tuned using grid search cross-validation (CV). The models are evaluated on various metrics using 5-fold CV. The most relevant brain regions from the optimal feature set are identified by ranking the SVM-RFE feature weights. The SVM-RFE approach achieved a state-of-the-art accuracy of 90.33% on the linear SVM model using the data Processing Assistant for Resting-State Functional Magnetic Resonance Imaging pipeline. The SVM model’s ability to rank the features used based on their importance provides clarity into the factors contributing to the diagnosis. The thalamus right, rectus right, and temporal middle left AAL brain regions, among others, were identified as having the highest number of connections to other brain regions. These results highlight the importance of using traditional ML models fo

关键词： Diseases

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Survey on age-related macular degeneration detection in OCT image

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International Journal of Computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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HybridFlicks: integrating collaborative filtering and content-based methods for enhanced movie recommendations

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Multimedia Tools and Applications 2025年 1-15页

作者： Dakshinamoorthy, Prabakar Murugan, Arvindh Vijayabhaskar, Sohan Thirumal, Bharani Kumar Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

User engagement has been improved by using recommender systems, which are essential for giving user recommendations. Matrix factorization (MF), one of the traditional approaches, has shown a promising work in capturing user preferences, especially in certain areas like movie recommendation with the help of MovieLens, which are datasets. Nevertheless, there is always a need to optimize long-term user engagement to overcome the challenges like large action space in recommender systems and the recent advancements like deep reinforcement learning (DRL) have shown a much greater role in addressing the same. This study aims to explore the effectiveness of hybrid approaches, wherein our innovative HybridFlicks recommender system is proposed, which combines the strengths of MF-based collaborative filtering with content-based filtering using TF-IDF and Cosine similarity, to improve the accuracy of the recommendations provided to the user, by focusing on enhancing the recall value and F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Reinforcement learning

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