Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

“…Apart from that, various classification algorithms, such as machine learning and deep learning, are useful for diagnosing MRI scans. …”
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A Smart Intelligence System for Hen Breed and Disease classification using Extra Tree classifier-based Ensemble Technique by Galib Muhammad Shahriar Himel, Md. Masudul Islam

“…The findings of this study represent a groundbreaking accomplishment in the realm of hen breed classification using machine learning, distinguishing this study as both state-of-the-art and pioneering. …”
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Kisspeptin is elevated in the brain after intracerebral haemorrhagic stroke by Saumya Maheshwari, In Hwa Um, Struan Donachie, Nafeesa Asghar, Karina McDade, Tracey Millar, David J. Harrison, Javier A. Tello

“…Here we report for the first time that kisspeptin immunoreactivity is significantly higher in post-mortem human brain tissue after both ICH and ICH associated with cerebral amyloid angiopathy. Machine learning and artificial intelligence-enabled image analysis of multiplexed immunolabeled brain tissues demonstrated that kisspeptin immunoreactivity was higher in cells of the microvasculature (CD105+), but not in neurons or astrocytes when compared to controls. …”
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Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Comparative analysis revealed that CNN-based approaches showed consistently high performance in ossification of the posterior longitudinal ligament detection, while traditional machine learning methods demonstrated varying effectiveness in cervical spondylotic myelopathy classification. …”
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Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature by Abbas Mardani, Mehrbakhsh Nilashi, Jurgita Antucheviciene, Madjid Tavana, Romualdas Bausys, Othman Ibrahim

“…Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. …”
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XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System by Maiada M. Mahmoud, Yasser Omar Youssef, Ayman A. Abdel-Hamid

“…The challenge is further compounded by the fact that most IDS rely on black-box machine learning (ML) and deep learning (DL) models, making it difficult for security teams to interpret their decisions. …”
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CAT-RFE: ensemble detection framework for click fraud by Yixiang LU, Guanggang GENG, Zhiwei YAN, Xiaomin ZHU, Xinchang ZHANG

“…Click fraud is one of the most common methods of cybercrime in recent years, and the Internet advertising industry suffers huge losses every year because of click fraud.In order to effectively detect fraudulent clicks within massive clicks, a variety of features that fully combine the relationship between advertising clicks and time attributes were constructed.Besides, an ensemble learning framework for click fraud detection was proposed, namely CAT-RFE ensemble learning framework.The CAT-RFE ensemble learning framework consisted of three parts: base classifier, recursive feature elimination (RFE) and voting ensemble learning.Among them, the gradient boosting model suitable for category features-CatBoost was used as the base classifier.RFE was a feature selection method based on greedy strategy, which can select a better feature combination from multiple sets of features.Voting ensemble learning was a learning method that combined the results of multiple base classifiers by voting.The framework obtained multiple sets of optimal feature combinations in the feature space through CatBoost and RFE, and then integrated the training results under these feature combinations through voting to obtain integrated click fraud detection results.The framework adopted the same base classifier and ensemble learning method, which not only overcame the problem of unsatisfactory integrated results due to the mutual constraints of different classifiers, but also overcame the tendency of RFE to fall into a local optimal solution when selecting features, so that it had better detection ability.The performance evaluation and comparative experimental results on the actual Internet click fraud dataset show that the click fraud detection ability of the CAT-RFE ensemble learning framework exceeds that of the CatBoost method, the combined method of CatBoost and RFE, and other machine learning methods, proving that the framework has good competitiveness.The proposed framework provides a feasible solution for Internet advertising click fraud detection.…”
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Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index by Ying Zhao, Dongli Hu, Dongxia Huang, You Liu, Zitong Yang, Lei Mao, Chao Liu, Fangfang Zhou

“…The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicting the placement positions of unsorted data elements in a sorted sequence based on machine learning models. Learned sorting pioneers a new generation of sorting algorithms and shows a great potential because of a theoretical time complexity ON and easy access to hardware-driven accelerating approaches. …”
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Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces by Pin-Chia Huang, I-Cheng Li, Ching-Yi Wang, Cheng-Hsiung Shih, Masimukku Srinivaas, Wan-Ting Yang, Chin-Fang Kao, Te-Jen Su

“…In recent years, AI technology has made significant advancements in image recognition, machine learning, and data analysis, which provide new opportunities for art management. …”
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Self‐Driving Lab for Solid‐Phase Extraction Process Optimization and Application to Nucleic Acid Purification by Sebastian Putz, Jonathan Döttling, Tim Ballweg, Andre Tschöpe, Vitaly Biniyaminov, Matthias Franzreb

“…Through the integration of robotics, machine learning, and data‐driven experimentation, the SDL demonstrates a highly accelerated process optimization with minimal human intervention. …”
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Binary plant rhizome growth-based optimization algorithm: an efficient high-dimensional feature selection approach by Jin Zhang, Fu Yan, Jianqiang Yang

“…Abstract Feature selection is a pivotal research area within machine learning, tasked with pinpointing the essential subset of features from a broad array that critically influences a model’s predictive capabilities. …”
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Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment by Arunit Maity, P. Prakasam, Sarthak Bhargava

“…Design/methodology/approach – A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. …”
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Groundbreaking Technologies and the Biocontrol of Fungal Vascular Plant Pathogens by Carmen Gómez-Lama Cabanás, Jesús Mercado-Blanco

“…Furthermore, artificial intelligence and machine learning can revolutionize crop protection through early disease detection, the prediction of disease outbreaks, and precision in BCA treatments. …”
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Generalized cross-entropy for learning from crowds based on correlated chained Gaussian processes by J. Gil-González, G. Daza-Santacoloma, D. Cárdenas-Peña, A. Orozco-Gutiérrez, A. Álvarez-Meza

“…Machine learning applications heavily depend on labeled data provided by domain experts to train accurate models. …”
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Application of Multiattention Mechanism in Power System Branch Parameter Identification by Zhiwei Wang, Liguo Weng, Min Lu, Jun Liu, Lingling Pan

“…The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.…”
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Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network by Qing Xu, Guiying Yang, Xiaobin Yin, Tong Sun

“…Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. …”
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Error Compensation for Dead Reckoning Based on SVM by Xin LI, Xiaoming WANG, Jianguo WU, Jiwei ZHAO, Jiacheng XIN, Kai CHEN, Bin ZHANG

“…In the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. …”
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Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy by Fabio Pisano, Giuliana Sias, Alessandra Fanni, Barbara Cannas, António Dourado, Barbara Pisano, Cesar A. Teixeira

“…In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. …”
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FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing by Kangning Yin, Xinhui Ji, Yan Wang, Zhiguo Wang

“…Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. …”
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Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer by Kenji Yokotani, Tetsuya Yamamoto, Hideyuki Takahashi, Masahiro Takamura, Nobuhito Abe

“…Hence, we propose an objective digital measurement method using a transformer, a state-of-the-art machine learning approach, to detect total gambling venue visitations for gamblers who visit multiple gambling venues using sounds in gamblers’ environments. …”
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