Article
Computer Science, Information Systems
J. Hemalatha, M. Sekar, Chandan Kumar, Adnan Gutub, Aditya Kumar Sahu
Summary: This study presents a three-step process to accurately identify clean and stego images, solving the issue of less favorable detection accuracy. It uses methods such as curvelet denoising and extracting Third-order Markov-chain sample transition probability matrices to increase detection accuracy. The oblique decision tree ensemble with a multisurface proximal support vector machine classifier is utilized to achieve higher detection accuracy than state-of-the-art classifiers.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2023)
Article
Biology
Zixuan Wang, Meiqin Gong, Yuhang Liu, Shuwen Xiong, Maocheng Wang, Jiliu Zhou, Yongqing Zhang
Summary: This paper provides a comprehensive compendium to better understand TF-DNA binding from genomic features. It summarizes commonly used datasets and data processing methods, and classifies and analyzes current deep learning methods in TFBS prediction. It also illustrates the characterization of functional consequences of TF-DNA binding and discusses the challenges and opportunities of deep learning in TF-DNA binding prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemical Research Methods
Mohammad Neamul Kabir, Limsoon Wong
Summary: This study presents a novel method called EnsembleFam that aims to improve function prediction for proteins in the twilight zone. By extracting core characteristics and using SVM classifiers, EnsembleFam achieves better accuracy in identifying proteins with low sequence homology compared to existing methods.
BMC BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Salah Eddine Bekhouche, Yassine Ruichek, Fadi Dornaika
Summary: Monitoring driver's drowsiness is crucial for road safety. This paper presents a computer vision-based framework for driver drowsiness detection, which detects the driver's face, extracts deep features, applies temporal feature aggregation and feature selection, and uses a binary classifier to determine drowsiness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shtwai Alsubai
Summary: This study explores the application of feature-based datasets and feature engineering in tooth caries detection. The experimental results show that the proposed method using PCA features and a voting classifier ensemble outperforms other approaches in terms of accuracy. The study provides new methods to improve dental healthcare and is of significant importance in evaluating the effectiveness of innovative approaches to address prevalent oral health issues.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Junaid Amin, Arvind Selwal, Ambreen Sabha
Summary: Saffron adulteration is a concerning issue due to the limited supply and popularity of saffron. To address this problem, researchers propose an ensemble model (SaffNet) based on image features to detect contamination in Kashmiri saffron. The SaffNet model, evaluated on a dataset collected from Kashmir valley, achieves an overall accuracy of 98%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Genetics & Heredity
Samuel Anyaso-Samuel, Archie Sachdeva, Subharup Guha, Somnath Datta
Summary: This study utilized microbiome samples from urban environments to predict the geographical location of unknown samples, implemented multiple classifiers and a robust ensemble approach, and highlighted the unreliability of relying on a single classification algorithm for metagenomic samples. By combining several classifiers via ensemble approach, the study achieved classification results comparable to the best-performing component classifier.
FRONTIERS IN GENETICS
(2021)
Article
Chemistry, Multidisciplinary
Anum Rauf, Aqsa Kiran, Malik Tahir Hassan, Sajid Mahmood, Ghulam Mustafa, Moongu Jeon
Summary: Heart-related diseases, especially caused by hypertension, are major causes of fatalities worldwide. Extracting bioactive peptides from natural food sources can provide potential alternatives to existing drugs with fewer side effects. In-silico approaches have been proven to be effective in identifying antihypertensive peptides, saving time and money. The proposed deep learning-based methodology combining CNN and SVM classifiers for feature extraction yields high accuracy in identifying antihypertensive peptides, outperforming existing state-of-the-art methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Biochemical Research Methods
Yanyi Chu, Aman Chandra Kaushik, Xiangeng Wang, Wei Wang, Yufang Zhang, Xiaoqi Shan, Dennis Russell Salahub, Yi Xiong, Dong-Qing Wei
Summary: Drug-target interactions are crucial in drug discovery, but existing prediction methods suffer from low precision and high false-positive rates. The proposed DTI-CDF model significantly outperforms traditional methods and accurately predicts new DTIs.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Deba Prasad Dash, Maheshkumar H. Kolekar, Kamlesh Jha
Summary: The paper proposes a technique for automatic detection of epileptic seizures using an online EEG database, achieving good classification accuracy with the K-nearest neighbor classifier. The method was validated with different types of seizures and achieved high accuracy on various datasets. Additionally, support vector machine classifier was evaluated and showed high accuracy in seizure detection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Zhiliang Zeng, Shouwei Zhao, Yu Peng, Xiang Hu, Zhixiang Yin
Summary: This paper discusses the application of single-cell RNA sequencing technology in the study of cellular dynamics, focusing on the analysis of RNA velocity in single-cell transcriptomics. By developing a cascade forest model, the authors successfully predict the RNA velocity of cells and achieve better results compared to other classifiers. This paper provides guidance for researchers in selecting and applying appropriate classification tools, and suggests possible directions for future improvement of classification tools.
Article
Computer Science, Artificial Intelligence
Nazeef Ul Haq, Bilal Tahir, Samar Firdous, Muhammad Amir Mehmood
Summary: Survival prediction is a critical task in clinical medicine, and this article evaluates the performance of different features and methods for predicting the survival of cancer patients. The study highlights the importance of age as a significant feature for survival prediction.
PEERJ COMPUTER SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Talha Karadeniz, Hadi Hakan Maras, Gul Tokdemir, Halit Ergezer
Summary: This paper introduces two novel methods for predicting heart disease, which use the kurtosis of features and the Maxwell-Boltzmann distribution. The Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. The proposed classifiers, GKMVB and MKMVB, outperform SVM, ANN, and Naive Bayes algorithms, indicating promising results. The experiments conducted on Statlog and Spectf datasets show optimized precision of 85.6 and 81.0, respectively, proving the effectiveness of the methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics, Interdisciplinary Applications
Yongjiao Sun, Yaning Song, Baiyou Qiao, Boyang Li
Summary: Research shows that predicting typhoon tracks is an important topic due to the disastrous effects typhoons often have. Existing studies have not fully considered historical and future factors, leaving room for improving prediction accuracy. The authors proposed a novel framework and verified its effectiveness on real datasets.
Article
Neurosciences
Li Yang, Jiaxiu He, Ding Liu, Wen Zheng, Zhi Song
Summary: This study analyzed microstate epileptic EEG to aid in the diagnosis and identification of epilepsy. Researchers found that microstate parameters can effectively classify epileptic EEG with an accuracy of 87.18%. Features extracted from the EEG can also be used to recognize interictal epilepsy with an accuracy of 79.55%. Microstate parameters combined with EEG features can be effectively used in epileptic EEG classification.
Article
Biochemical Research Methods
Jiandong Ding, Shuigeng Zhou, Jihong Guan
BMC BIOINFORMATICS
(2011)
Proceedings Paper
Computer Science, Artificial Intelligence
Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou
Summary: The paper proposes a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN, which is an iterative process consisting of constructing keyword graph, training subgraph annotator, training text classifier, and re-extracting keywords from classified texts. Extensive experiments show that the proposed method outperforms existing ones on both long-text and short-text datasets.
2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021)
(2021)
Article
Biotechnology & Applied Microbiology
Linxia Wan, Jiandong Ding, Ting Jin, Jihong Guan, Shuigeng Zhou
Article
Biotechnology & Applied Microbiology
Jiandong Ding, Danqing Li, Uwe Ohler, Jihong Guan, Shuigeng Zhou