Accelerating AI-Based Battery Management System’s SOC and SOH on FPGA by Satyashil D. Nagarale, B. P. Patil

“…This article presents an experimental study for the artificial intelligence (AI)-based data-driven prediction of lithium battery parameters SOC and SOH with the help of deep learning algorithms such as Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM). …”
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Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

“…PurposeThis study aims to develop hippocampal texture model for predicting cognitive impairment in middle-aged patients with cerebral small vessel disease (CSVD).MethodsThe dataset included 145 CSVD patients (Age, 52.662 ± 5.151) and 99 control subjects (Age, 52.576±4.885). An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. …”
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Habitat Distributions and Abundance of Four Wild Herbivores on the Qinghai–Tibetan Plateau: A Review by Tian Qiao, Chiwei Xiao, Zhiming Feng, Junzhi Ye

“…Furthermore, we critically compare three aspects of methods: transect surveys, machine learning (ML), and deep learning (DL) methods of studying WHs. The results show that since the 1990s, the distributions and abundance of WHs have exhibited a trend of initial decline followed by recovery, largely attributed to global climate warming and a decrease in illegal hunting. …”
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An optimized lightweight real-time detection network model for IoT embedded devices by Rongjun Chen, Peixian Wang, Binfan Lin, Leijun Wang, Xianxian Zeng, Xianglei Hu, Jun Yuan, Jiawen Li, Jinchang Ren, Huimin Zhao

“…YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. …”
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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images by Sultan Refa Alotaibi, Manal Abdullah Alohali, Mashael Maashi, Hamed Alqahtani, Moneerah Alotaibi, Ahmed Mahmud

“…Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. This study introduces a novel Colorectal Cancer Diagnosis using the Optimal Deep Feature Fusion Approach on Biomedical Images (CCD-ODFFBI) method. …”
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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering by Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He

“…However, there is as yet no research combining collaborative filtering and content-based recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. …”
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Prediction of Future Terrorist Activities Using Deep Neural Networks by M. Irfan Uddin, Nazir Zada, Furqan Aziz, Yousaf Saeed, Asim Zeb, Syed Atif Ali Shah, Mahmoud Ahmad Al-Khasawneh, Marwan Mahmoud

“…Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. …”
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Searching for the Ideal Recipe for Preparing Synthetic Data in the Multi-Object Detection Problem by Michał Staniszewski, Aleksander Kempski, Michał Marczyk, Marek Socha, Paweł Foszner, Mateusz Cebula, Agnieszka Labus, Michał Cogiel, Dominik Golba

“…The advancement of deep learning methods across various applications has forced the creation of enormous training datasets. …”
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Mid-infrared spectra of dried and roasted cocoa (Theobroma cacao L.): A dataset for machine learning-based classification of cocoa varieties and prediction of theobromine and caffe... by Gentil A. Collazos-Escobar, Andrés F. Bahamón-Monje, Nelson Gutiérrez-Guzmán

“…This dataset provides a basis for further research, enabling the integration of mid-infrared spectral data with HPLC (as a standard) to fine-tune machine learning and deep learning models that could be used to simultaneously predict the theobromine and caffeine content, as well as cocoa variety in both dried and roasted cocoa samples using a non-destructive approach based on spectral data. …”
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A Novel Telerehabilitation System for Physical Exercise Monitoring in Elderly Healthcare by Muhammad Abrar Ashraf, Shaheryar Najam, Touseef Sadiq, Shabbab Algamdi, Hanan Aljuaid, Hameedur Rahman, Ahmad Jalal

“…These multimodal features are fused and input into a deep learning model for classification and correctness assessment. …”
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Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) by Nurilmiyanti Wardhani, Billy Eden William Asrul, Antonius Riman Tampang, Sitti Zuhriyah, Abdul Latief Arda

“…This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. …”
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Profile to frontal face recognition in the wild using coupled conditional generative adversarial network by Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi

“…Abstract In recent years, with the advent of deep‐learning, face recognition (FR) has achieved exceptional success. …”
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Multimodal sequential cross-modal transformer for predicting plant available water capacity (PAWC) from time series of weather and crop biological data by Dung Nguyen, Peter de Voil, Andries Potgieter, Yash P. Dang, Thomas G. Orton, Duc Thanh Nguyen, Thanh Thi Nguyen, Scott C. Chapman

“…Deep learning (DL) and machine learning (ML) have been applied widely to satellite data of vegetation indices to infer indirect features associated with soil characteristics that affect crop performance in rain-fed environments. …”
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SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion by Qianwen Xiong, Xiaoyuan Ren, Huanyu Yin, Libing Jiang, Canyu Wang, Zhuang Wang

“…However, traditional methods and unsupervised deep-learning methods always exhibit inherent limitations in dealing with complex overlapping regions. …”
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SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence by G. Sathish Kumar, E. Suganya, S. Sountharrajan, Balamurugan Balusamy, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…Deep hyper optimization combines deep learning models with hyperparameter tuning to automatically identify the most relevant features, optimizing model accuracy and reducing dimensionality. …”
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Advanced colon cancer detection: Integrating context-aware multi-image fusion (Camif) in a multi-stage framework by M.V.R. Vittal

“…The proposed approach uses advanced deep learning models to integrate pre-processing, segmentation, and classification. …”
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Peripheral nerve injury induces dystonia-like movements and dysregulation in the energy metabolism: A multi-omics descriptive study in Thap1+/− mice by Colette Reinhold, Susanne Knorr, Rhonda L. McFleder, Lisa Harder-Rauschenberger, Tom Gräfenhan, Andreas Schlosser, Michael Sendtner, Jens Volkmann, Chi Wang Ip

“…Phenotypic analysis using an unbiased deep learning algorithm revealed that nerve-injured Thap1+/− mice exhibited significantly more dystonia like movements (DLM) over the course of the 12-week experiment compared to naive Thap1+/− mice. …”
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SVFRH: A Growth Stage-Based Compartmental Model for Predicting the Disease Incident in Tomato (Solanum lycopersicum) by Athira P. Shaji, S. Hemalatha

“…Over decades, research has primarily focused on addressing these challenges through computer vision and deep learning techniques. In this work, we employ a comprehensive modelling approach that combines compartmental and logistic regression models to thoroughly address disease dynamics in tomato crops, with a particular focus on tomato early blight diseases. …”
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