Evaluating Gender Bias in Computational AI-Based Approaches

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 217 - 244

Published: Feb. 25, 2025

Ethics in computational AI-based approaches have become an inclusive topic of interest among researchers when artificial intelligence has come to light. Doubts always existed against the ethical implications introducing society. With advent intelligence, these voices only grown louder. Over years, many guidelines and policies been introduced ensure network security, economic development, quality life. However, impact whether they would be sustainable is still under consideration. There are too positions where biased data sets algorithms used. The outline this research arranged a way that allows analysis algorithm used by well-known application. being here amassed from varied set people, rigorous inspection occurred produce thorough results.

Language: Английский

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Human-Technology Interaction

Advances in educational technologies and instructional design book series, Journal Year: 2025, Volume and Issue: unknown, P. 391 - 420

Published: Feb. 14, 2025

Integrating AI-powered adaptive frameworks speaks to a transformative enhancement in instruction, upgrading customized picking up information of things about and optimizing instruction. The paper addresses imperative ethical issues, which envelop records security, algorithmic predisposition, the need for straightforwardness, highlighting significance reasonable AI making, beyond any doubt, an impartial rite passage simple directions conceivable outcomes. By perusing impact on instructive homes student engagement, underscores capability versatile analyzing structures bridge holes guidelines best indent openness. improvement their suggestions instruction are expressed, specializing interior creating innovation usefulness revolutionize thinking. concludes with rules partners ensure AI's viable moral arrangement adjusting innovative development human-centered procedures make comprehensive even-handed know-how situations.

Language: Английский

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A systematic review of Machine Learning and Deep Learning Approaches in Plant Species Detection

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100605 - 100605

Published: Oct. 1, 2024

Language: Английский

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Evaluate the Performance of Deep CNN Algorithm based on Parameters and Various Geometrical Attacks

Wireless Personal Communications, Journal Year: 2023, Volume and Issue: 132(4), P. 2587 - 2602

Published: Sept. 26, 2023

Language: Английский

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BivalveNet: A hybrid deep neural network for common cockle (Cerastoderma edule) geographical traceability based on shell image analysis

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102344 - 102344

Published: Oct. 20, 2023

Bivalve traceability is a major concern. It of utmost importance to develop tools that allow providing important information the consumer, not only on origin product but also its sustainability and safety, due harvest restrictions imposed by regulatory entities. This study evaluated application computer vision machine learning technologies for efficiently discriminating cockle harvesting based shell geometric morphometric analysis, improving methodologies in these organisms, highlighting potential low-cost techniques as reliable tool. Thirty Cerastoderma edule samples were collected along five locations Atlantic West South Portuguese coast with individual images processed using lazysnapping segmentation, spectro-textural-morphological phenotype extraction, feature selection through hybrid Principal Component Analysis Neighborhood which resulted R, a*, b*, entropy, diameter. Three approaches models developed tested: pre-trained networks (EfficientNet-Bo, ResNet101, MobileNetV2, InceptionV3) numerical inputs (Approach 1), image-based 2), deep neural Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM) 3). Based test results, Approach 3 GRU-LSTM-BiLSTM sequence exhibited highest accuracy (96.91%) sensitivity (96%) among other thirteen models, hence, named BivalveNet. Comparing attained from BivalveNet mollusc studies, it was observed an efficiency close standard destructive, time-consuming, expensive techniques, making highly advantageous approach common geographical available bivalve species.

Language: Английский

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MFFGD: An adaptive Caputo fractional-order gradient algorithm for DNN

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 128606 - 128606

Published: Sept. 1, 2024

Language: Английский

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Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area

Ecological Informatics, Journal Year: 2024, Volume and Issue: 83, P. 102848 - 102848

Published: Oct. 9, 2024

Language: Английский

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AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach

Baghdad Science Journal, Journal Year: 2023, Volume and Issue: 20(6(Suppl.)), P. 2624 - 2624

Published: Dec. 5, 2023

Cassava, a significant crop in Africa, Asia, and South America, is staple food for millions. However, classifying cassava species using conventional color, texture, shape features inefficient, as leaves exhibit similarities across different types, including toxic non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet feature extractor accurately classify four types cassava: Gajah, Manggu, Kapok, Beracun. The dataset was collected from local farms Lamongan Indonesia. To collect images agricultural experts, consists 1,400 images, each type has 350 images. Three fully connected (FC) layers were utilized extraction, namely fc6, fc7, fc8. classifiers employed support vector machine (SVM), k-nearest neighbors (KNN), Naive Bayes. study demonstrated that most effective extraction layer achieving an accuracy 90.7% SVM. SVM outperformed KNN Bayes, exhibiting 90.7%, sensitivity 83.5%, specificity 93.7%, F1-score 83.5%. successfully addressed challenges leveraging methods, specifically fc6 AlexNet. proposed approach holds promise enhancing plant techniques, benefiting researchers, farmers, environmentalists identification, ecosystem monitoring, management.

Language: Английский

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Improved Whale Optimization Algorithm for Cluster Analysis

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 156 - 166

Published: Nov. 11, 2024

Language: Английский

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Exploring Diverse Techniques in Image and Video Forgery

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 318 - 328

Published: Nov. 11, 2024

Language: Английский

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