Article
Computer Science, Information Systems
Gazal, Kapil Juneja
Summary: This paper investigates a two-level filter-based hybrid model to accurately identify spam messages. The model selects the most important features through filtering and evaluation methods, and uses classifiers to generate probabilistic scores for spam detection. The experimental results show that the model achieves high accuracy on multiple datasets and outperforms traditional methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
K. Meena, N. N. Krishna Veni, B. S. Deepapriya, P. A. Harsha Vardhini, B. J. D. Kalyani, L. Sharmila
Summary: Skin diseases have a significant impact on both the physical and psychological health of patients, and accurately predicting the disease cases is crucial for effective treatment. However, selecting appropriate features from the vast amount of healthcare data available is challenging. This study aims to identify significant attributes and remove irrelevant features to improve the performance of the model.
Article
Biology
Asmaa H. Rabie, Ahmed Saleh, Nehal A. Mansour
Summary: This paper introduces a Covid-19's Integrated Herd Immunity (CIHI) strategy, aiming to keep society safe with minimal losses even in the presence of Covid-19. It achieves this by accurately predicting asymptomatic cases who will be infected by the virus and by taking suitable precautions for those predicted to be severely affected before actual infection takes place. The strategy utilizes a Distance Based Classification Strategy (DBCS) to classify individuals into different types based on their vulnerability to Covid-19, providing the best classification accuracy among recent Covid-19 diagnosing techniques.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Computer Science, Artificial Intelligence
Carlos Villa-Blanco, Concha Bielza, Pedro Larranaga
Summary: Real-world problems often have high feature dimensionality, making it difficult to model and analyze the data. Feature subset selection (FSS) techniques can be used to reduce irrelevant or redundant information, improving the speed and performance of building models. This review focuses on incremental FSS algorithms that can efficiently handle large volumes of data received sequentially. Different strategies, such as updating feature weights incrementally, applying information theory, or using rough set-based FSS, are discussed, along with various supervised and unsupervised learning tasks where FSS is applicable.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Multidisciplinary Sciences
Hafiz Abbad Ur Rehman, Chyi-Yeu Lin, Zohaib Mushtaq, Shun-Feng Su
Summary: This study suggests using efficient classifiers with machine learning algorithms to detect and diagnose thyroid disease. The experiment showed that classifiers using L-1-based feature selection achieved higher accuracy compared to other techniques.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Savita Wadhawan, Raman Maini
Summary: Cardiac disease is a leading cause of death worldwide. The lack of radiologists and doctors is a barrier to early diagnosis. Computational intelligence can assist in diagnosing diseases and relieving the workload of medical professionals. This study proposes an effective technique for cardiac disease prediction based on machine intelligence. With accurate data generation and feature selection, the proposed technique improves the accuracy of prediction models and outperforms baseline approaches in terms of accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Analytical
Majed Alwateer, Abdulqader M. Almars, Kareem N. Areed, Mostafa A. Elhosseini, Amira Y. Haikal, Mahmoud Badawy
Summary: A novel approach for processing healthcare data is introduced in this paper to predict useful information with minimum computational cost, aiming to improve accuracy and reduce processing time. The proposed method utilizes the Whale Optimization Algorithm and Naive Bayes Classifier for data processing and feature selection, resulting in enhanced accuracy and processing speed.
Article
Genetics & Heredity
Yuxin Guo, Liping Hou, Wen Zhu, Peng Wang
Summary: The study focuses on the characteristics and identification methods of hormone binding proteins, successfully establishing a prediction model HBP_NB, using high-quality dataset and feature selection algorithm to accurately identify HBPs.
FRONTIERS IN GENETICS
(2021)
Article
Computer Science, Artificial Intelligence
Karunakaran Velswamy, Rajasekar Velswamy, Iwin Thanakumar Joseph Swamidason, Selvan Chinnaiyan
Summary: This study utilizes a modified bee algorithm for attribute selection to optimize the classification model for heart disease prediction, aiming to improve classification accuracy.
Article
Engineering, Multidisciplinary
M. Shaheen, N. Naheed, A. Ahsan
Summary: Big data analytics uncovers hidden patterns through classification, prediction and reinforcement of big datasets. Relevant, important and informative features are selected using different filtration techniques. A new feature selection technique called Relevance-diversity algorithm and a new supervised classification algorithm based on Naive Bayes classification are proposed. The performance of these techniques is evaluated using various datasets, and the results show improvements in terms of feature selection, accuracy, and time complexity.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Chemistry, Physical
Maryam K. Ghassemi, Sahar Barzegari, Parastoo Hajian, Hanieh Zham, Hamid Reza Mirzaei, Farshad H. Shirazi
Summary: In this study, FTIR-ATR spectroscopy was used to compare gastric samples, and data modeling was performed using PCA, SVM, and KNN algorithms. Specific peaks related to malignancy were identified in malignant tissue, which can be used to distinguish between normal and malignant samples.
JOURNAL OF MOLECULAR STRUCTURE
(2021)
Article
Computer Science, Artificial Intelligence
Hongpo Zhang, Ning Cheng, Yang Zhang, Zhanbo Li
Summary: Label flipping attack is a poisoning attack that reduces the classification performance of a model by flipping the labels of training samples. Naive Bayes algorithm demonstrates good robustness in handling issues like document classification and spam filtering. The proposed label flipping attacks effectively reduce the accuracy of various classification models.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xin Zhang, Hongshan Xiao, Ruize Gao, Hongwu Zhang, Yu Wang
Summary: K-Nearest Neighbors (KNN) rule is a powerful classification technique but has drawbacks. In this study, an improved KNN rule, IKNN_PSLFW, combining prototype selection and local feature weighting, is proposed to address these issues and achieve promising classification performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Arti Rana, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Nazir Ahmad, Manoj Kumar Panda
Summary: Parkinson's disease is a neurodegenerative disease that is difficult to diagnose. This research proposes a new diagnostic method using supervised classification algorithms, with high accuracy.
Article
Environmental Sciences
Long Cui, Jiahua Zhang, Zhenjiang Wu, Lan Xun, Xiaopeng Wang, Shichao Zhang, Yun Bai, Sha Zhang, Shanshan Yang, Qi Liu
Summary: Wetlands in the Yellow River Delta are important and vulnerable due to tidal action and sediment deposits. A object-oriented approach with feature preference machine learning was used to classify the wetlands. A superpixel segmentation method using the watershed algorithm improved the classification accuracy. The random forest classifier combining superpixel segmentation and feature selection methods outperformed other pixel-based machine learning methods with a 91.74% overall accuracy and a kappa coefficient of 0.9078.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Automation & Control Systems
H. Anandakumar, K. Umamaheswari
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2018)
Article
Computer Science, Information Systems
V. Priya, K. Umamaheswari
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2019)
Article
Computer Science, Hardware & Architecture
H. Anandakumar, K. Umamaheswari
COMPUTERS & ELECTRICAL ENGINEERING
(2018)
Article
Engineering, Multidisciplinary
T. Vairam, S. Sarathambekai, K. Umamaheswari
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2018)
Article
Computer Science, Information Systems
V Priya, K. Umamaheswari
JOURNAL OF INFORMATION SCIENCE
(2020)
Article
Computer Science, Information Systems
M. Kalpana Devi, K. Umamaheswari
Summary: This study aims to design an effective spectrum handoff scheme using SpecBPSO algorithm and M/G/1 queuing model, and enhance the efficiency of SU by using Cluster Based Cooperative Spectrum Sensing. By dynamically selecting cluster heads based on SU sensing signals and associating them with SU base stations, better reporting of active and inactive channels in the spectrum can be achieved.
Article
Engineering, Electrical & Electronic
M. Kalpana Devi, K. Umamaheswari
Summary: The study focuses on effective spectrum management in CRN, addressing challenges such as spectrum sensing, reducing handoff frequency, and increasing throughput. The proposed DHHO-DVSM algorithm optimizes channel selection and handoff models to improve transmission efficiency, with a 22.8% improvement over E-CRNs and a 41% improvement over SpecPSO.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
P. Jayapriya, K. Umamaheswari
Summary: This research proposes an effective feature optimization technique using the K-nearest neighbor algorithm and differential evolution for finger knuckle print-based authentication. Experimental results show improved classification accuracy with this method.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Telecommunications
G. Niranjani, K. Umamaheswari
Summary: This study proposes a Tabu Search approach for solving the sustainable vehicle routing problem. Through testing and analysis, it is found that the method can effectively reduce sustainable costs and performs well on different performance measures.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
G. Niranjani, K. Umamaheswari
DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 1
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
S. P. Rajamohana, K. Umamaheswari, K. Karunya, R. Deepika
DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 1
(2020)
Proceedings Paper
Computer Science, Hardware & Architecture
H. Anandakumar, K. Umamaheswari, R. Arulmurugan
INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS AND COMMUNICATION TECHNOLOGIES (ICCNCT 2018)
(2019)
Article
Computer Science, Hardware & Architecture
S. Sarathambekai, K. Umamaheswari
Article
Computer Science, Hardware & Architecture
Jia Ke, Ying Wang, Mingyue Fan, Xiaojun Chen, Wenlong Zhang, Jianping Gou
Summary: This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Shi Qiu, Huping Ye, Xiaohan Liao, Benyue Zhang, Miao Zhang, Zimu Zeng
Summary: This paper proposes a multilevel-based algorithm for hyperspectral image interpretation, which achieves semantic segmentation through multidimensional information fusion, and introduces a context interpretation module to improve detection performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jianteng Xu, Qingguo Bai, Zhiwen Li, Lili Zhao
Summary: This study constructs two optimization models for the omnichannel closed-loop supply chain by leveraging the combined power of leader-follower game and mean-variance theories. The focus is on analyzing the performance of manufacturers who distribute products through physical stores. The results show that the risk-averse attitude of the physical store has a positive impact on the overall system profitability, but if the introduced physical store belongs to another firm, total profit experiences a decline.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jiahao Xiong, Weihua Ou, Zhonghua Liu, Jianping Gou, Wenjun Xiao, Haitao Liu
Summary: This paper proposes a novel remote photoplethysmography framework, named GraphPhys, which utilizes graph neural network to extract physiological signals and introduces Average Relative GraphConv for the task of remote physiological signal measurement. Experimental results show that the methods based on GraphPhys significantly outperform the original methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Zhiyao Tong, Yiyi Hu, Chi Jiang, Yin Zhang
Summary: The rise of illicit activities involving blockchain digital currencies has become a growing concern. In order to prevent illegal activities, this study combines financial risk control with machine learning to identify and predict the risks of users with poor credit. Experimental results demonstrate high performance in user financial credit analysis.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)