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Improved Precision Crop Yield Prediction Using Weighted-Feature Hybrid SVM: Analysis of ML algorithms

IETE JOURNAL OF RESEARCH 2024年第3期70卷 2628-2640页

作者： Pavani, S. Beulet, Augusta Sophy P. Vellore Inst Technol VIT Sch Elect Engn Chennai 600127 India

As the world's population grows, the agricultural sectors are destined to increase crop production and security. Improving crop yield by using advanced technologies gives remarkable growth to the economy of the country. Agriculture provides over 20% of India's GDP. Using machine learning algorithms, the crop yield can be predicted which is useful to the farmers to plan the cultivation beforehand. In this work, various machine learning (ML) algorithms are applied to predict the yield of 'rice and sorghum (jowar)' and a novel weighted feature approach with a combination of Support Vector machine (SVM) and Random Forest (RF) is proposed for two Indian seasons. RF is used to select training data at random, and the learning rate approach from deep learning concepts is implemented to add random weights to each parameter;the SVM model is then trained using the weighted training data. The best weights are again applied for the whole data to implement the SVM and RF algorithms. The weighted feature hybrid model is compared with SVM, RF, Decision tree, Naive Bayes, and k-Nearest Neighbor algorithms. RF-based regression method is also implemented and its ability to predict the crop yield has been discussed based on its performance metrics. The results show that the proposed weighted feature hybrid SVM-RF model gives the best accuracy of 90% when compared with the traditional algorithms. Also, the performances of various ML algorithms for crop yield prediction are analysed and cross-validation of the models is performed and compared, which improved the accuracy by 8-10%.

关键词： Crop yield prediction performance metrics regression model supervised machine learning algorithms weighted features

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Classifying design-level requirements using machine learning for a recommender of interaction design patterns

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IET SOFTWARE 2020年第5期14卷 544-552页

作者： Silva-Rodriguez, Viridiana Edith Nava-Munoz, Sandra Castro, Luis A. Martinez-Perez, Francisco E. Perez-Gonzalez, Hector G. Torres-Reyes, Francisco Univ Autonoma San Luis Potosi Fac Ingn Dr Manuel Nava 8 San Luis Potosi San Luis Potosi Mexico Inst Tecnol Sonora Dept Computac & Diseno Obregon Mexico

Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design-level requirements analysis. The success of the software system is mostly dependent on how well users' requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human-computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design-level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers' decision making when designing user interfaces.

关键词： learning (artificial intelligence) user interfaces human computer interaction pattern classification object-oriented methods interactive systems decision making software quality user interface design design-level requirements analysis software system interactive system design process human-computer interactions design task interaction design patterns recommendation model design-level requirements classification supervised machine learning algorithms

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Predicting Forced Blower Failures Using machine learning algorithms and Vibration Data for Effective Maintenance Strategies

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2023年第5期23卷 2191-2203页

作者： Salem, Khaled AbdelGwad, Ebtisam Kouta, Hanan Port Said Univ Prod Engn & Mech Design Dept Fac Engn Port Said Egypt

The emergence of Industry 4.0, also known as the fourth industrial revolution, has brought forth the concept of prognostics and health management (PHM) as an inevitable trend in the realm of industrial big data and smart manufacturing. This study aims to present a proof-of-concept that illustrates how machine learning can be employed to analyze industrial facility data and anticipate the condition of industrial machines. Specifically, a comprehensive case study focusing on vibration monitoring is conducted. The proposed models aim to predict maintenance requirements for the forced blower of a chemical plant by utilizing vibration data obtained during the manufacturing process. To validate the methodology, five different machine learning algorithms, namely logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and random forest (RF), are employed. The evaluation metrics used include Matthews correlation coefficient (MCC) and receiver operator characteristic curve (ROC). This study aims to establish a relationship between machine failures caused by vibration and the prediction of both healthy and faulty bearings using the machine learning approaches. The findings indicate that the XGBoost algorithm outperforms other approaches with an MCC of 0.800 and a higher area under the ROC curve.

关键词： Industrial machine supervised machine learning algorithms Vibration data Case study Predictive maintenance

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Identification of Fake Reviews Using Semantic and Behavioral Features 4

Identification of Fake Reviews Using Semantic and Behavioral...

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4th International Conference on Information Management (ICIM)

作者： Wang, Xinyue Zhang, Xianguo Jiang, Chengzhi Liu, Haihang Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781538661475

In recent years, online reviews have been playing an important role in making purchase decisions. This is because, these reviews can provide customers with large amounts of useful information about the goods or service. However, to promote factitiously or lower the quality of the products or services, spammers may forge and produce fake reviews. Due to such behavior of the spammers, customers would be misleaded and make wrong decisions. Thus detecting fake (spam) reviews is a significant problem. In this paper, we propose two types of features and apply supervised machine learning algorithms for performing classification on Yelp's real-life data. In terms of features used, there are two new semantic feature sets: readability features and topic features. Our results show that our proposed new features are more effective than n-gram features in detecting spam reviews. To improve classification on the real Yelp review data, we use a set of behavioral features about reviewers and their reviews for learning, which dramatically improves the classification result on real-life opinion spam data. For further improvement, we also ensure the number of reviewers instead of reviews is balanced.

关键词： fake reviews semantic and behavioral features supervised machine learning algorithms

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Quality-Efficiency Trade-offs in machine learning for Text Processing 5

Quality-Efficiency Trade-offs in Machine Learning for Text P...

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IEEE International Conference on Big Data (IEEE Big Data)

作者： Baeza-Yates, Ricardo Liaghat, Zeinab Univ Pompeu Fabra DTIC Web Sci & Social Comp Grp Barcelona Catalonia Spain

ISBN: (纸本)9781538627150

As the amount of available digital documents keeps growing rapidly, extracting useful information from them has become a major challenge. Data mining, natural language processing, and machine learning are powerful techniques that can be used together to deal with this problem. Depending on the task at hand, there are many different approaches that can be used. The methods available are continuously improved, but not all of them have been tested and compared in a set of coherent problems using supervised machine learning algorithms. For example, what happens to the quality of the methods if we increase the training data size from, say, 100 MB to over 1 GB? Moreover, are quality gains worth it when the rate of data processing diminishes? Can we trade quality for time efficiency and recover the quality loss by just being able to process more data? We attempt to answer these questions in a general way for text processing tasks, considering the trade-offs involving training data size, learning time, and quality obtained. For this, we propose a performance trade-off framework and apply it to three important tasks: Named Entity Recognition, Sentiment Analysis and Document Classification. These problems were also chosen because they have different levels of object granularity: words, paragraphs, and documents. For each problem, we selected several supervised machine learning algorithms and we evaluated the trade-offs of them on large publicly available data sets (news, reviews, patents). To explore these trade-offs, we use different data subsets of increasing size ranging from 50 MB to several GB. For the last two tasks, we also consider similar algorithms with two different data sets and two evaluation techniques, to study their impact on the resulting trade-offs. We find that the results do not change significantly and that most of the time the best algorithms are the ones with fastest processing time. However, we also show that the results for small data (say less than 1

关键词： supervised machine learning algorithms text processing algorithmic trade-offs learning trade-offs

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Toxicity Detection Using State of the Art Natural Language Methodologies 8

Toxicity Detection Using State of the Art Natural Language M...

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8th International Conference on Control and Robotics Engineering (ICCRE)

作者： Keskin, Enes Faruk Acikgoz, Erkut Dogan, Gulustan Turkish Aeronaut Assoc Univ Dept Comp Engn Ankara Turkiye Atilim Univ Dept Ind Engn Ankara Turkiye Univ North Carolina Wilmington Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9798350345650

In this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.

关键词： language models bert transformers natural language processing supervised machine learning algorithms deep learning

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Detection of Adulteration in Fruits Using machine learning 6

Detection of Adulteration in Fruits Using Machine Learning

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6th International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： Brighty, S. Prince Sahaya Harini, G. Shri Vishal, N. Sri Ramakrishna Engn Coll Dept CSE Coimbatore Tamil Nadu India

ISBN: (纸本)9781665440868

Food is essential for life. The food we take should be pure, nutritious and free from any type of adulteration for proper maintenance of human health. In this paper, an IoT based food and formalin detection technique is developed to detect the presence of formalin using machine-learning approaches. Volatile compound HCHO gas sensor connected with Raspberry pi3 were used to extract the concentration of the formalin as a function of output voltage of any fruit or vegetable and different machine learning algorithms were used to classify the fruit or vegetable based on their extracted features. supervised machine learning algorithms have been incorporated in our system to accurately predict the correct concentration of formalin at all temperatures which is also able to correctly classify between artificially added and naturally formed formalin.

关键词： Adulteration formalin naturally occurring formalin formaldehyde machine learning IoT HCHO gas sensor Raspberry Pi3 supervised machine learning algorithms

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Flow graphs as data structures for inducing classifiers

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International Journal of Hybrid Intelligent Systems 2019年第2期15卷 77-90页

作者： Nicoletti, Maria Do Carmo Rodrigues, Emilio Carlos Centro Universitário C. Limpo Paulista C.L. Paulista SP Brazil Universidade Federal de S. Carlos S. Carlos SP Brazil Instituto Federal de São Paulo Bragança Paulista SP Brazil

This paper describes an empirical research work based on the use of a suitable data structure, named Flow Graph (FG), that can be induced from a supervised training data set. A FG can be approached as a weighted and labeled digraph that summarizes a given supervised training set, aiming at its analysis. FGs can also be used as a repository of the information embedded in training sets, that supports the extraction of classification rules, aiming at the definition of classifiers. The work described in this paper reviews FGs and related concepts, as originally proposed i.e., a suitable structure for modeling discrete data, and proposes its customization for dealing with continuous data. The customization consists of a pre-processing step where a discretization process is carried out in a two-step hybrid approach named HFG (Hybrid Flow Graph). Several experiments with focus on the classifiers extracted from HFGs were conducted and their results were analyzed with focus on both, the value of some metrics associated with the induced digraph-based structure as well as the performance of the classifier extracted from the structure. For the experiments 19 diversified datasets were used and the classification results were comparatively analyzed with those obtained by classifiers induced using four other algorithms namely, J48, Naïve Bayes, k-Nearest-Neighbor and Support Vector machine. © 2019 - IOS Press and the authors. All rights reserved.

关键词： data discretization data structures extended flow graphs Flow graphs hybrid systems supervised machine learning algorithms

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A Pipelined Framework for the Prediction of Cardiac Disease with Dimensionality Reduction 1

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2nd International Conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Shobana, G. Subramanian, Nalini Madras Christian Coll Dept Comp Applicat Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Informat Technol Thandalam India

ISBN: (数字)9783031281839

ISBN: (纸本)9783031281822;9783031281839

Cardiac diseases are diseases that affect people across the globe, and cardiac failure occurs without any warning. Identification of cardiac diseases at an early stage becomes a challenge for researchers in the health domain. machine learning frameworks and algorithms are effectively used in the current medical field to predict and classify various diseases accurately. In this paper, we explore the traditional supervised machine learning techniques and algorithms and their cardiac disease classification accuracy. We further investigate the feature extraction technique Kernel Principal Component Analysis with a pipelined framework. The proposed framework overcomes the issue of overfitting and increases the prediction accuracy most effectively. Random Forest produced the most perfect result and Extreme Gradient Boost technique achieved an accuracy of 99.02%. Other Boosting classifiers, Gradient Boosting and Light Gradient Boosted machine produced an accuracy of 94.16% and 98.38% respectively.

关键词： supervised machine learning algorithms Kernel principal component analysis Cardiac disease Light gradient boosting Random forest

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Ensemble based machine learning Approach for Predicting Customer Churn in E-Commerce 26

Ensemble based Machine Learning Approach for Predicting Cust...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Masroor, Mohammad Irtiza Sadman Sakib, K.M. Hasan, Mahmudul Al Arman, Md Abdullah Dey, Pronay Sanam, Tahsina Farah Ahsanullah University of Science & Technology Dhaka Bangladesh Northern University Bangladesh Dhaka Bangladesh Bangladesh University of Engineering & Technology Dhaka Bangladesh

ISBN: (纸本)9798350359015

Due to the intense competition among E-Commerce industries, customers have a large selection of products to choose from and as a result, these industries experience customer churn. Since retaining existing customers than acquiring new ones is cost effective, customer churn prediction is key for the E-commerce industry. The goal of this paper is to forecast customer churn for a ride sharing application by training appropriate supervised ensemble-based machine learning models including Gradient Boost, Cat Boost and XG Boost on a known dataset containing 50,000 known samples of many such customer attributes as well as their loyalty to the application. A variety of manual preprocessing approaches, such as removing missing data, duplicate data, outliers, etc., were employed to reduce the high-class imbalance in the raw dataset while also preventing useful information loss in the already small dataset, enabling the models to predict customer churn with neutrality. Standardization techniques was also utilized to normalize the data so as to restrict the wide distribution of each feature and improve model performance. The AUC-ROC performance metric was used, which met our objectives while also working well with imbalanced datasets. Gradient Boost and Cat boost algorithms performed the best in identifying potential churning customers, with AUC-ROC scores of 0.85 and computation times of 58 seconds and 80 seconds respectively, while XG boost was not far behind with an AUC-ROC score of 0.84 and the fastest computation time of 45 seconds among all models assessed. © 2023 IEEE.

关键词： Class imbalance Customer Churn supervised machine learning algorithms

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