Insights into hepatic angiomyolipoma: A case report highlighting scannographic findings by Fatima Zohra EL Yaagoubi, MD, Said Adnor, MD, Adam Hajjine, MD, Soukaina Wakrim

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Prospects of Agrotourism Development in the Region by Bahodirhon Safarov, Akmal Amirov, Nargiza Mansurova, Thowayeb H. Hassan, Habibullo Hasanov, Ana Cornelia Pereș, Bahadur Bilalov, Khasan Turdibekov

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Deep learning-based prediction of multi-level just noticeable distortion by Haifeng XU, Hongkui WANG, Haibing YIN, Chuqiao CHEN

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Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features by Windra Swastika, Kestrilia Rega Prilianti, Paulus Lucky Tirma Irawan, Hendry Setiawan

“…We propose: (1) a Random Forest classifier using carefully engineered morphological and textural features, and (2) a Convolutional Neural Network (CNN)architecture for automated feature learning from microscopic blood cell images. …”
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Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. …”
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A Dual-channel Progressive Graph Convolutional Network via subgraph sampling by Wenrui Guan, Xun Wang

“…To enhance the representation power, we construct a dual channel fusion module by using both the geometric information of the node feature and the original topology. Specifically, we evaluate the complementary information of the dual channels based on the joint entropy between the feature information and the adjacency matrix, and effectively reduce the information redundancy by reasonably selecting the feature information. …”
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A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network by Xing Ding, Xiangrong Zhang, Chao Liang, Bo Liu, Lanjie Niu

“…The study begins with the construction of pixel-level fusion networks, feature-weighted fusion networks and the multimode mutual attention fusion network. …”
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PB-STR: A spatiotemporal transformer network for multi-behavior recognition of pigs by Yufan Hu, Xiaobo Wang, Rui Mao, Yusen Guo, Xianyao Zhu, Meili Wang

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An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network by Tingzhong Wang, Honghao Xu, Yudong Hai, Yutian Cui, Ziyuan Chen

“…To address these issues, an improved crop disease identification method based on convolutional neural network is proposed to process images of crops for identifying diseases. …”
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Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City by Yanni Liu, Dongsheng Liu, Yuwei Chen

“…Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. …”
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An optimized stacking-based TinyML model for attack detection in IoT networks. by Anshika Sharma, Shalli Rani, Mohammad Shabaz

“…Some amount of data preprocessing has been done applying methods such as label encoding, feature selection, and data standardization. A stacking ensemble learning technique uses multiple models combining lightweight Decision Tree (DT) and small Neural Network (NN) to aggregate power of the system and generalize. …”
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Adaptive Context-Aware Generative Adversarial Network for Low-quality Image Enhancement by Xingyu Pan, Fengling Chen

“…FGAN-DA comprises the dual attention feature extraction, invertible flow generation network, the Markov discriminant network. …”
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Research on incentive mechanism integrated trust management for P2P networks by HU Jian-li1, ZHOU Bin2, WU Quan-yuan2

“…An important challenge regarding peers’ trust valuation in peer-to-peer（P2P） systems was how to cope with such issues as dishonest feedbacks from malicious peers,collusions and complicatedly strategic frauds to the trust model itself,which could not be effectively tackled by the existing solutions.Thus,an incentive mechanism integrated trust management model for P2P networks,（IMTM） was proposed,to quantify and evaluate the trustworthiness of peers.Moreover,the related definitions and distributed implementation strategies of IMTM was also given.In IMTM,the recommendation credibility,composed of three factors,including the similarity characteristics,the altering scope of interaction experiences and the time fading feature of trust when interacting,was introduced to portray the extent to which the trustor trusted another recommender’s recommendations.Besides,the trusted service persistent intensity was imported to this model to promote peers to provide high-quality services to others continuously,and the risk mechanism was used to improve the sensitivity of this model.Additionally,based on the trust model,an adaptive reputation based incentive mechanism was presented.Through this mechanism,trusted peers could migrate to the centric position,while untrusted peers to the edge of the topology,incenting peers to provide more high-quality services in order to get more return on services.Theoretical analyses and experimental results demonstrate that IMTM has advantages in combating such malicious behaviors as the dishonest recommendations from malicious peers,collusions and the complicatedly strategic attacks to the trust model itself over the existing models,and show more adaptability,sensitivity and effectiveness.…”
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Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models by W. Beniel Dennyson, C. Jothikumar

“…Abstract Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. …”
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Cross-Scale Hypergraph Neural Networks with Inter–Intra Constraints for Mitosis Detection by Jincheng Li, Danyang Dong, Yihui Zhan, Guanren Zhu, Hengshuo Zhang, Xing Xie, Lingling Yang

“…To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. …”
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DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model by Md. Ashikur Rahman, Md. Mamun Ali, Kawsar Ahmed, Imran Mahmud, Francis M. Bui, Li Chen, Santosh Kumar, Mohammad Ali Moni

“…This study introduces DeepQSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. …”
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Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network by Asroni Asroni, Ku Ruhana Ku-Mahamud, Cahya Damarjati, Hasan Basri Slamat

“…To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. …”
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An Efficient Deep Learning-Based Framework for Predicting Cyber Violence in Social Networks by Younes Fayand Fathabad, Mohammad Ali Balafar, Amin Golzari Oskouei, Kamal Koohi

“…The Jaccard similarity metric is employed to construct neighborhoods of input texts, allowing the model to leverage surrounding context for improved feature extraction. The proposed model combines Bi-LSTM and GRU networks to capture both sequential dependencies and contextual relationships effectively. …”
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Exploring the performance of LBP-capsule networks with K-Means routing on complex images by Patrick Mensah Kwabena, Benjamin Asubam Weyori, Ayidzoe Abra Mighty

“…Capsule Networks (CapsNets) were proposed to mitigate the shortcomings of Convolutional Neural Networks (CNNs) such as invariance. …”
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Protecting IOT Networks Through AI-Based Solutions and Fractional Tchebichef Moments by Islam S. Fathi, Hanin Ardah, Gaber Hassan, Mohammed Aly

“…The effectiveness of our proposed technique for detecting IoT threats was evaluated using the UNSW-NB15 and Bot-IoT datasets, featuring illustrative cases of common IoT attack scenarios, such as DDoS, identity spoofing, network reconnaissance, and unauthorized data access. …”
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