Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms by Jay Prakash Singh, Debolina Ghosh, Jagannath Singh, Anurag Bhattacharjee, Mahendra Kumar Gourisaria

“…Despite problems such as possible over-reliance on pre-trained features and computational complexity, the modified DenseNet-121 is useful for accurate mushroom classification. …”
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Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers by Arafet Sbei, Khaoula ElBedoui, Walid Barhoumi

“…This efficiency is attributed to the distillation process that retains essential features while reducing computational demands. Notably, DistilBERT achieved an overall accuracy of 0.86, significantly surpassing BERT’s 0.55, GPT-2’s 0.36, XLNet’s 0.51, Ernie 2.0’s 0.72, Electra’s 0.74, ALBERT’s 0.72, and RoBERTa’s 0.71. …”
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Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics by Quiin Latifah Almatin Lubis, Arif Akbarul Huda

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Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty by Muqiao Cai

“…CSA is used to tune the network weights and feature parameters worldwide to improve the neural model’s convergence speed and predictive ability. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

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Desirable Elements for a Particle System Interface by Daniel Schroeder, Howard J. Hamilton

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TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images by S. Ledbin Vini, P. Rathika

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Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification by Gst. Ayu Vida Mastrika Giri, Ngurah Agus Sanjaya ER, I Ketut Gede Suhartana

“…The mean, standard deviation, skewness, kurtosis, power spectral density, zero-crossing rate, and root mean square were extracted as statistical features from the raw EEG data. After parameter tuning, the GRU-based model achieved an excellent average accuracy value of 95.94% and also yielded precision, recall, and F1-scores within the range of 0.95 to 0.97 over 5-fold cross-validation. …”
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Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method by Febiana Angela tanesab, Rangga Pahlevi Putra, Aviv Yuniar Rahman

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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

“…Based on the geological understanding that different attributes computed from the same sample convey correlated features reflecting geological properties, we pre‐train the network on large‐scale unlabeled data using a self‐supervised learning strategy of attribute masking and recovery. …”
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Enhancing task execution: a dual-layer approach with multi-queue adaptive priority scheduling by Mansoor Iqbal, Muhammad Umar Shafiq, Shouzab Khan, Obaidullah, Saad Alahmari, Zahid Ullah

“…Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. …”
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Enhanced YOLOv8 for Efficient Parcel Identification in Disordered Logistics Environments by Han Yu, Zhang Fengshou, Zhuang Gaoshuai, Qu Yuanhao, He Aohui, Duan Qingyang

“…The proposed algorithm introduces the C2f-OR module to reduce parameters and computation, the Conv-Ghost module for efficient feature extraction, and the HIoU loss function to enhance identification accuracy. …”
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Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images by Mouhamed Laid Abimouloud, Khaled Bensid, Mohamed Elleuch, Mohamed Ben Ammar, Monji Kherallah

“…This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training, while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification. …”
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MDMU-Net: 3D multi-dimensional decoupled multi-scale U-Net for pancreatic cancer segmentation by Lian Lu, Miao Wu, Gan Sen, Fei Ren, Tao Hu

“…While maintaining clinically viable precision, the model significantly improves computational efficiency, with parameter count (26.97M) and FLOPs (84.837G) reduced by 65.5% and 71%, respectively, compared to UNETR, providing reliable algorithmic support for precise diagnosis and treatment of pancreatic cancer.…”
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A semi-mechanistic mathematical framework for simulating multi-hormone dynamics in reproductive endocrinology by Alexandre Vallée, Anis Feki, Gaby Moawad, Jean-Marc Ayoubi

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Automatic Real-Time Generation of Floor Plans Based on Squarified Treemaps Algorithm by Fernando Marson, Soraia Raupp Musse

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Prediction of Shear Capacity of FRP Reinforced Concrete Beams Using Deep Neural Networks Based on Hyperparameter Optimization by Mohammed Sharif

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Improving Gene Expression Programming Method by Najla Al-Saati, Nidhal Al-Assady

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Gorilla troops optimization with deep learning based crop recommendation and yield prediction by A. Punitha, V. Geetha

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Capacity Evaluation for IEEE 802.16e Mobile WiMAX by Chakchai So-In, Raj Jain, Abdel-Karim Tamimi

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