Hybrid Android Malware Detection and Classification Using Deep Neural Networks by Muhammad Umar Rashid, Shahnawaz Qureshi, Abdullah Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh

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Robust Deep Neural Network for Classification of Diseases from Paddy Fields by Karthick Mookkandi, Malaya Kumar Nath

“…The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. …”
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An adaptive filter for anemia screening using deep convolutional neural network by Jose B. Lazaro, Jr., Jennifer C. Dela Cruz, Jocelyn F. Villaverde

“…This study introduces an automated anemia detection system powered by deep convolutional neural networks (DCNNs), CMOS image sensing, Adam optimizer, and multi-scale feature extraction (MSFE) to improve diagnostic precision and accessibility. …”
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Interpretable classification of Levantine ceramic thin sections via neural networks by Sara Capriotti, Alessio Devoto, Simone Scardapane, Silvano Mignardi, Laura Medeghini

“…Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petrographic analysis is time-consuming. …”
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Neuropathic Pain Detection: An EEG-Based Brain Functional Network Approach by Abdulyekeen T. Adebisi, Ho-Won Lee, Kalyana C. Veluvolu

“…By extracting persistent homology features from these networks, we developed a support vector machine (SVM)-based classifier that distinguishes control subjects from those experiencing varying degrees of NP. …”
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A Combined CNN-LSTM Network for Ship Classification on SAR Images by Abdelmalek Toumi, Jean-Christophe Cexus, Ali Khenchaf, Mahdi Abid

“…However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images. …”
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Super-resolution reconstruction of mine image based on generative adversarial network by Fan ZHANG, Ying LIU, Hui SONG, Jiarong ZHANG, Haixing CHENG

“…Aiming at the degradation phenomenon of mine images, in order to improve the resolution of mine images, a super-resolution reconstruction method mine image based on generative adversarial network is proposed. Based on SRGAN, this method improves the network structure and loss function. …”
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MANET Routing Protocols’ Performance Assessment Under Dynamic Network Conditions by Ibrahim Mohsen Selim, Naglaa Sayed Abdelrehem, Walaa M. Alayed, Hesham M. Elbadawy, Rowayda A. Sadek

“…The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. …”
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Detection of human activities using multi-layer convolutional neural network by Essam Abdellatef, Rasha M. Al-Makhlasawy, Wafaa A. Shalaby

“…This paper introduces HARCNN, a novel approach leveraging Convolutional Neural Networks (CNNs) to extract hierarchical spatial and temporal features from raw sensor data, enhancing activity recognition performance. …”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…Second is the spatio-channel attention network (SCAN), which has a neural architecture designed to focus on significant regions, such as spatial and channel regions, but not restricted to them. …”
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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…The main goal of the research presented in this paper is to evaluate the effectiveness of deep learning neural networks in detecting Deepfake audios in the Urdu language. …”
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Advancing Artificial Intelligence of Things Security: Integrating Feature Selection and Deep Learning for Real-Time Intrusion Detection by Faisal Albalwy, Muhannad Almohaimeed

“…Three classifiers—artificial neural networks (ANNs), deep neural networks (DNNs), and TabNet–were evaluated on the RT-IoT2022 dataset. …”
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Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data by Ryo Fujita, Yoshifumi Amamoto, Jun Kikuchi

“…BO of process conditions using these features achieved an optimization rate comparable to using material property values, suggesting that effective material design is possible without directly evaluating a large number of properties. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…However, current methods mostly use single-model architectures, including convolutional neural networks (CNNs), which might not adequately capture the long-range spatial correlations and local fine-grained features required for classification. …”
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Investigations on higher-order spherical harmonic input features for deep learning-based multiple speaker detection and localization by Nils Poschadel, Stephan Preihs, Jürgen Peissig

“…The trained neural networks, optimized with a single loss function for the combined tasks of detection and localization, are then evaluated in detail for overall SDL performance as well as their performance in the sub-tasks of detection and, particularly, localization. …”
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A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction by Wu Yan, Fu Yu, Li Tan, Li Mengshan, Xie Xiaojun, Zhou Weihong, Sheng Sheng, Wang Jun, Wu Fu-an

“…Results Here, we proposed a hybrid machine learning model based on graph convolutional neural networks (GCN) and bi-directional long short-term memory (Bi-LSTM) with attention mechanism and multidimensional multivariate feature coding for essential gene prediction, called EGP Hybrid-ML. …”
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Emo-SL Framework: Emoji Sentiment Lexicon Using Text-Based Features and Machine Learning for Sentiment Analysis by Manar Alfreihat, Omar Saad Almousa, Yahya Tashtoush, Anas AlSobeh, Khalid Mansour, Hazem Migdady

“…Recently, given the rise of types of social media networks, the analysis of sentiment and opinions in textual data has gained significant importance. …”
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EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms by Ao Zhang, Jianhua Jia, Mingwei Sun, Xin Wei

“…This model begins by encoding DNA sequences using pre-trained DNA embeddings and extracting local features through convolutional neural networks (CNNs). …”
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DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES by Diyari A. Hassan

“…This paper investigates the implementation of Convolutional Neural Networks (CNNs) for computing the singular values of both real and complex matrices. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…With the development of multi-modal convolutional neural networks (CNNs) in recent years, the performance of driving scene semantic segmentation algorithms has been dramatically improved. …”
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