Analysis and Prediction of the Survival of Titanic Passengers Using Machine Learning

Tabbakh, Amer; Rout, Jitendra Kumar; Rout, Minakhi

doi:10.1007/978-981-15-4218-3_29

Cited by 3 publications

(2 citation statements)

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“…It uses a majority voting technique using decisions from several DT, as shown in Figure 6. It overcomes the drawbacks the over-fitting of using a single DT by selecting random training data with replacement to build individual tree [40]. Well defined training data subset will be considered in order to generate DT model for each tree, with the leftover one of three equal parts of training data that which is used for assessing the accuracy of the model.…”

Section: Random Forestmentioning

confidence: 99%

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Tabbakh¹,

Barpanda²

2022

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In this paper, different types of plant diseases in the PlantVillage dataset are getting focused for classification. In the realm of machine vision, plant disease identification is one of the most crucial tasks in the agricultural sector. It is a technique that employs equipment to capture images to detect and classify different types of diseases in plants. However, nakedeye monitoring of plants is impractical due to long processing times and a lack of specialists on farms in remote locations. Hence, combining image processing techniques with machine learning provides a solution to the problem of agricultural production while also ensuring food security. The plant features are extracted using a modified gray-level co-occurrence matrix (GLCM) technique and based on various statistical features. Both of these approaches were applied on original images with background and segmented images without background. Wavelet transform is also used with segmented images to decompose the image into sub-bands. All the features obtained are combined and SMOTE technique is used to balance the dataset prior to classification. For the purpose of classification, six machine learning models were compared, namely Light Gradient Boosting Machine (LGBM), Random Forest (RF), Decision Trees (DT), Logistic Regression (LG), AdaBoost, and Support Vector Machine (SVM). Further, different combinations of features were experimented and the experimental results prove that employing LGBM and SVM models resulted in attaining higher accuracy values i.e. 94.39% and 93.15%, respectively.

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Section: Random Forestmentioning

confidence: 99%

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Tabbakh¹,

Barpanda²

2022

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“…See, for example,(Kakde and Agrawal, 2018;Barhoom et al, 2019;Singh et al, 2020;Tabbakh et al, 2021;Shekhar et al, 2021).…”

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confidence: 99%

Metaethical Perspectives on 'Benchmarking' AI Ethics

LaCroix¹,

Luccioni²

2022

Preprint

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Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research and have been developed for a variety of tasks ranging from question answering to facial recognition. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. In this paper, drawing upon research in moral philosophy and metaethics, we argue that it is impossible to develop such a benchmark. As such, alternative mechanisms are necessary for evaluating whether an AI system is 'ethical'. This is especially pressing in light of the prevalence of applied, industrial AI research. We argue that it makes more sense to talk about 'values' (and 'value alignment') rather than 'ethics' when considering the possible actions of present and future AI systems. We further highlight that, because values are unambiguously relative, focusing on values forces us to consider explicitly what the values are and whose values they are. Shifting the emphasis from ethics to values therefore gives rise to several new ways of understanding how researchers might advance research programmes for robustly safe or beneficial AI. We conclude by highlighting a number of possible ways forward for the field as a whole, and we advocate for different approaches towards more value-aligned AI research.

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