A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

“…Experimental performance is measured on the ISIC 2018 dataset which contains 3300 images of skin disease including benign and malignant type cancer images. 80% of the images from the ISIC 2018 dataset are allocated for training, while the remaining 20% are designated for testing. …”
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SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models by Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov, Vladimir A. Muliukha

“…According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…The methodology was tested on UCSD and Duke's OCT datasets and produced excellent results. The proposed SE-Improved Hybrid Model outperformed the current best-known approaches, with accuracy rates of 99.58% on the UCSD dataset and 99.18% on the Duke dataset.…”
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Aquatic Toxicology Notes: Diquat by P. Chris Wilson, Jun Wu

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Optimasi Data Tidak Seimbang pada Interaksi Drug Target dengan Sampling dan Ensemble Support Vector Machine by Nabila Sekar Ramadhanti, Wisnu Ananta Kusuma, Annisa Annisa

“…Metode ini sudah diuji pada dataset Nuclear Receptor, G-Protein Coupled Receptor dan Ion Channel dengan rasio ketidakseimbangannya sebesar 14.6%, 32.36%, dan 28.2%. …”
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Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet) by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam G. Tejani

“…The experimental results show the proposed model outperforms the other models of dataset 1 in accuracy of 0.988086 and 0.990469, precision of 0.987976 and 0.990628, recall of 0.988298 and 0.990435, with the lowest Hamming Loss of 0.011914 and 0.009531, also, the proposed model outperforms the other models of dataset 2 in accuracy of 0.9819 and 0.9919, precision of 0.9868 and 0.9968, recall of 0.9813 and 0.9923, with the lowest Hamming Loss of 0.0209 and 0.0109, thus the proposed model outperformed the other models in detecting zero-day exploits.…”
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BioLake: an RNA expression analysis framework for prostate cancer biomarker powered by data lakehouse by Qiaowang Li, Yaser Gamallat, Jon George Rokne, Tarek A. Bismar, Reda Alhajj

“…Unlike some existing analytical tools on the market, BioLake supports a wide range of web-based bioinformatics data analysis for public datasets, while allowing researchers to analyze their private datasets instantly. …”
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Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis by Syed Muhammad Ali Imran, Abida Hussain, Nema Salem, Muhammad Arsalan

“…Utilizing the publicly available Retinal Images for Pigment Signs (RIPS) dataset for retinal pigment detection and segmentation, 4-fold cross-validation tests were conducted to assess the suggested P3S-Net. …”
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A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques by G. Logeswari, J. Deepika Roselind, K. Tamilarasi, V. Nivethitha

“…Specifically, it achieves 98.83% accuracy, 98.56% precision, and 98.65% F-Measure on the TON-IoT dataset, and 98.6% accuracy, 98.5% precision, and 98.94% F-Measure on the BOT-IoT dataset, showcasing its superior performance over existing IDS models. …”
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Advanced deep learning techniques for recognition of dental implants by Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit sukhasare

“…After augmentation, a dataset of 1744 images was secured and then split into training, validation and test datasets for the model. …”
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BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. by Natalie R Davidson, Fan Zhang, Casey S Greene

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A Collaborative Data Network for the Asia Oceania Region Enabled by Emerging Technologies to Foster Innovation in a Secure and Open Environment by Alison Specht, Kim Bryceson, Shoufeng Cao, Margaret O’Brien, S. M. Guru, Pedro Pizzigatti Correa, Michelle Waycott

“…The use of blockchain will ensure the information about each dataset (metadata, provenance, and access rights) is immutably connected to each dataset. …”
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Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

“…Future work should focus on developing automated segmentation algorithms for M3M and IAC on PRs, to identify relevant anatomical structures, thereby improving clinical usability. The dataset, feature extraction, and ML codes are available through the CONTACT grand challenge.…”
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General practice database on mortality in adults on methylphenidate: cohort study by Bruno Stricker, Katia Verhamme, Kiki Cheung

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Differences in fatty infiltration in thigh muscles and physical function between people with and without knee osteoarthritis and similar body mass index: a cross-sectional study by Jessica B. Aily, Marcos de Noronha, Ricardo J. Ferrari, Stela M. Mattiello

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Traditional Chinese Medicine Prescription Generation Model Based on Search Enhancement by ZHAO Zijuan, REN Xueting, SONG Kai, QIANG Yan, ZHAO Juanjuan, ZHANG Junlong

“…Either large number of standardized dataset is needed to train the model, or the domain knowledge and expertise of TCM are required. …”
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Causal Effects of PM2.5, NOx, and NO2 on Cognitive Function: A Two-sample Mendelian Randomization Study by Yilin Tang, Zhenyu Li, Yicheng Fu, Mingyi Zhao

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Dying to be famous: retrospective cohort study of rock and pop star mortality and its association with adverse childhood experiences by Mark A Bellis, Karen Hughes, Olivia Sharples, Tom Hennell, Katherine A Hardcastle

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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. …”
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Etracarpidium Conophorum Extract Exhibits Anti-Fatigue Activity in Rats Via Reduced Protein Catabolism, Increased Antioxidant Status and Delayed Lactate Elevation by Ugochukwu Vincent, Igbokwe, Ejike, Daniel Eze, Moses Dele, Adams, Karimah Mohammed, Rabiu, Iliya, Ezekiel, Prisca Ojochogu, Ajeka, Adam Moyosore, Afodun

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