Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases by Saleem Malik, S. Gopal Krishna Patro, Chandrakanta Mahanty, Saravanapriya Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, Anjanabhargavi Kulkarni, Abdulrajak Buradi, Addisu Frinjo Emma, Naoufel Kraiem

“…Abstract In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). …”
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Solar Radio Burst Detection Based on Deformable DETR by Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu

“…The experimental results demonstrate that the proposed model achieves a mAP@50 of 83.5% and a recall rate of 99.4% on the SRBs data set. Additionally, the model exhibits excellent noise-robust performance and can efficiently detect and locate Type II, III, IV, and V SRBs. …”
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Detection of Abrupt Changes in Runoff in the Weihe River Basin by Yanling Li, Jianxia Chang, Zhiliang Wang, Huiling Li

“…The key part of the method is the empirical decomposition mode with which any complicated data set can be decomposed into small number of intrinsic mode functions that admit well adaptive Hilbert transforms. …”
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Role of liquid biopsy in the detection and monitoring of cervical cancer by E. V. Kayukova

“…The cytological screening is not always effective and appropriate, therefore the search for new predictive markers of the cervical cancer are of great importance. there are no biomarkers for monitoring patients previously treated for cervical cancer. liquid biopsy is a new option of personalized approach to the detection and monitoring of cervical cancer. it is a set of methods for determining the derivatives of a tumor in biological media, most often in the blood: circulating tumor cells, circulating dNa, RNa, exosomes, etc.The purpose of the studywas to analyze data on the role of liquid biopsy in the diagnosis and monitoring of cervical cancer.Material and methods. …”
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In silico detection of sequence variations modifying transcriptional regulation. by Malin C Andersen, Pär G Engström, Stuart Lithwick, David Arenillas, Per Eriksson, Boris Lenhard, Wyeth W Wasserman, Jacob Odeberg

“…The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. …”
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SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT by Kamran Sattar Awaisi, Qiang Ye, Srinivas Sampalli

“…Consequently, it can accurately detect the health status of sensor data, and if the sensor data is faulty, it can specifically identify the fault type. …”
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Detection of pediatric developmental delay with machine learning technologies. by Shin-Bo Chen, Chi-Hung Huang, Sheng-Chin Weng, Yen-Jen Oyang

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Feature-based enhanced boosting algorithm for depression detection by Muhammad Sadiq Rohei, Kasturi Dewi Varathan, Shivakumara Palaiahnakote, Nor Badrul Anuar

“…Fortunately, machine learning and deep learning techniques have demonstrated excellent results in the early detection of depression using social media data. Most recently, researchers have utilized boosting algorithms including pre-defined boosting algorithms or built their own boosting algorithm for the detection of depression. …”
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Bladder cancer detection in urine by novel methylation markers by Irene J. Beijert, Birgit M. M. Wever, Anouk E. Hentschel, Yara van den Burgt, Paul C. Kauer, Birgit I. Lissenberg-Witte, R. Jeroen A. van Moorselaar, Renske D. M. Steenbergen, Jakko A. Nieuwenhuijzen

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Strengthening cancer care for the LGBTQIA+ population in Nepal: A narrative review to set priorities for equitable oncology services by Sunil Shrestha, Nabin Pathak, Simit Sapkota, Sudip Thapa, Subhas Pandit, Jeebana Bhandari, Pankaj Barman, Pratik Khanal, Kamal Ranabhat, Vibhu Paudyal, Deependra Singh

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Frequency, Clinical Features, and Imaging Comparisons of Seropositive versus Seronegative Autoimmune Encephalitis in a Tertiary Care Setting by Bhavani Madduluri, Shaik A Jabeen, Reshma S Shaik, Sireesha Yareeda, Suryaprabha Turaga, Madhur K Srivastava, Charan Neeradi

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The abnormal traffic detection scheme based on PCA and SSH by Zhenhui Wang, Dezhi Han, Ming Li, Han Liu, Mingming Cui

“…Simulation experiments based on IDS2017 and IDS2012 data sets are carried out in this paper. Experimental results show that PCSS is obviously superior to other detection models in detection speed and accuracy, which provides a new method for efficiently detecting traffic attacks.…”
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An Alliance of Carbapenem-Resistant Klebsiella pneumoniae with Precise Capsular Serotypes and Clinical Determinants: A Disquietude in Hospital Setting by Elghar Soltani, Alka Hasani, Mohammad Ahangarzadeh Rezaee, Maryam Zaare Nahandi, Akbar Hasani, Pourya Gholizadeh

“…Clinical and demographic data of all patients were reviewed including age, gender, underlying diseases, and the treatment obtained. …”
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Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection by Geeta Rani, Atharv Kothekar, Shawn George Philip, Vijaypal Singh Dhaka, Ester Zumpano, Ester Zumpano, Eugenio Vocaturo, Eugenio Vocaturo

“…Moreover, it utilizes the unique features of transformer and Linformer models to enhance the robustness and generalization of deepfake detection techniques. The low computational requirement and high accuracy of 98.9% increase the real-time applicability of the model, preventing blackmail and other losses to the public.DiscussionThe proposed hybrid model utilizes the strength of the transformer model in capturing complex patterns in data. …”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction by İrem Üstek, Miguel Arana‐Catania, Alexander Farr, Ivan Petrunin

“…This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire prediction using autoencoders and clustering techniques for anomaly detection. Historical weather and normalized difference vegetation index data sets of Australia for 2005–2021 were utilized. …”
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Automatic detection of pupil reactions in cataract surgery videos. by Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer, Stephanie Sarny, Doris Putzgruber-Adamitsch, Yosuf El-Shabrawi

“…This would be especially true if large data files could be evaluated automatically. In this work, we automatically detect pupil reactions in cataract surgery videos. …”
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Virological evidence of the impact of non-pharmaceutical interventions against COVID-19 in Ecuador, a resource-limited setting by Andres Moreira-Soto, Alfredo Bruno, Doménica de Mora, Michelle Paez, Jimmy Garces, Ben Wulf, Anna-Lena Sander, Maritza Olmedo, Maria José Basantes Mantilla, Manuel Gonzalez Gonzalez, Solon Alberto Orlando, Silvia Salgado Cisneros, Juan Carlos Zevallos, Jan Felix Drexler

“…Ecuador had substantial COVID-19-mortality during 2020 despite early implementation of non-pharmaceutical interventions (NPIs). Resource-limited settings like Ecuador have high proportions of informal labour which entail high human mobility, questioning efficacy of NPIs. …”
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Strawberry Disease Detection Using Multispectral UAV Imagery by A. F. Cheshkova, V. S. Riksen

“…(Results and discussion) Analysis of the multispectral data enabled the identification of informative feature sets for distinguishing between healthy and fungus-infected strawberry plants. …”
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