Article
Computer Science, Information Systems
D. Cenitta, R. Vijaya Arjunan, K. V. Prema
Summary: This study proposes an improved squirrel search optimization algorithm to address the feature selection problem in ischemic heart disease datasets and improve decision-making. Through a comparison with other state-of-the-art optimization algorithms, the proposed algorithm achieves a feature selection accuracy of over 98%.
Article
Computer Science, Artificial Intelligence
Behrouz Samieiyan, Poorya MohammadiNasab, Mostafa Abbas Mollaei, Fahimeh Hajizadeh, Mohammadreza Kangavari
Summary: Feature selection techniques are crucial for simplifying problems, improving performance, and optimizing computational efficiency while ensuring interpretability. This study presents a novel feature selection algorithm based on the crow search algorithm, which optimizes the balance between global and local search processes and achieves significant feature reduction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Niam Abdulmunim Al-Thanoon, Zakariya Yahya Algamal, Omar Saber Qasim
Summary: Feature selection plays a crucial role in improving classification algorithm results. The OBL-BCSA algorithm, utilizing an opposition-based learning strategy, excels in selecting relevant features with high classification performance.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Fan Cheng, Junjie Cui, Qijun Wang, Lei Zhang
Summary: Evolutionary algorithms (EAs) have proven to be competitive in solving feature selection problem. However, existing EA-based FS methods suffer from the exponential increase of search space with increasing number of features, making them unsuitable for high-dimensional data classification. In this article, a variable granularity search-based multiobjective EA (VGS-MOEA) is proposed to address this issue by using a group of features to represent one bit in the individual representation, greatly reducing the search space. Experimental results on 12 high-dimensional data sets have shown that VGS-MOEA outperforms existing methods in terms of classification accuracy, selected features, and running time.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Shanshan Si, Weiping Ding, Xinya Wang, Jiucheng Xu
Summary: This paper proposes a multiobjective sparrow search feature selection approach to address the challenges of balancing convergence and diversity in nondominated solutions. The approach combines the updating formula of observers with the mutualism phase of the symbiotic organisms search algorithm to improve the search ability. The paper also introduces sparrow ranking, feature ranking, and a preference information-based mutation algorithm to enhance the diversity of solutions and guide the population towards better solutions.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Multidisciplinary
Yanan Zhang, Chunwu Wei, Juanjuan Zhao, Yan Qiang, Wei Wu, Zifan Hao
Summary: This paper presents a novel Adaptive Mutation Quantum-inspired Squirrel Search Algorithm (AM-QSSA) that improves population diversity and search efficiency by introducing quantum behavior and mutation strategy, and solves the problem of premature convergence. Through analysis of multiple evaluation criteria, it shows the efficiency and stability of AM-QSSA in optimization problems.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Chun-Cheng Lin, Jia-Rong Kang, Yu-Lin Liang, Chih-Chi Kuo
Summary: This paper proposes a hybrid memetic algorithm combined with variable neighborhood search for simultaneous instance and feature selection, which performs well in addressing big noisy data problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Nairita Sarkar, Pankaj Kumar Keserwani, Mahesh Chandra Govil
Summary: Utilizing the cloud environment for running business is common in IT organizations due to its flexible services for users. However, the distributed and open nature of cloud computing makes it vulnerable to various known and unknown attacks, leading to privacy and security concerns. In this study, a better and fast Intrusion Detection System (IDS) is proposed for anomaly detection in the cloud network environment. The proposed IDS model combines Improved squirrel search algorithm (ISSA) and Modified-deep belief network (MDBN) to handle high dimensional network traffic data and address the imbalanced nature of the dataset. The evaluation results show that the proposed IDS model achieves the highest accuracy (99.8%) and lowest false alarm rate (0.02%) on the UNSW-NB15 dataset.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Aman Bhaskar, Chirag Sharma, Khalid Mohiuddin, Aman Singh, Osman A. Nasr, Mamdooh Alwetaishi
Summary: The paper discusses a smart video watermarking technique that combines meta-heuristic algorithms and embedding methods to optimize efficiency and robustness. The main goal of the optimization algorithm is to achieve solutions with maximum robustness within set quality thresholds. By employing a popular video watermarking technique, the proposed system shows improved accuracy and resilience.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Lin Sun, Shanshan Si, Weiping Ding, Jiucheng Xu, Yan Zhang
Summary: A novel adaptive particle swarm algorithm (NSSA) is studied and applied to feature selection problems. The traditional SSA algorithm is improved by introducing a chaotic mapping scheme and a reverse learning scheme to enhance global search ability and prevent trapping into local optima. NSSA is combined with S-shaped and V-shaped transfer functions to design two types of BSSFS methods, and experimental results show that NSSA outperforms other methods in terms of optimization effectiveness, while FSBSV outperforms other comparative algorithms in terms of classification effectiveness and robustness.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad
Summary: A new two-phase gene selection method for microarray data is proposed in this study. This method reduces the number of genes significantly and improves the classification accuracy through anomaly detection and guided genetic algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Biology
Xinlu Tang, Zhanfeng Mo, Cheng Chang, Xiaohua Qian
Summary: Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. We propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. The proposed method outperforms advanced regularization methods, especially in rejecting isolated methylation sites, providing an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Rajesh Ranjan, Jitender Kumar Chhabra
Summary: This study proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism. MO-CSACFS is implemented over several datasets to assess its performance, and it is compared with other similar algorithms. The results show that MO-CSACFS produces compact and robust clusters comparable to other works from the literature.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Rama Krishna Eluri, Nagaraju Devarakonda
Summary: Feature selection is an important data preprocessing tool in data mining, aiming to maximize the model's generalization and minimize the feature size. This paper proposes a hybrid binary flamingo search with a genetic algorithm (HBFS-GA) to solve the feature selection problem. Experimental results demonstrate that the proposed method outperforms existing models in multiple metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Miguel Garcia-Torres, Roberto Ruiz, Federico Divina
Summary: Feature selection is a challenging task due to high data dimensionality, but feature grouping and metaheuristic algorithms can help overcome this. This work proposes a Scatter Search strategy that uses feature grouping to generate diverse and high quality solutions. The strategy not only finds the best subset of features, but also reduces data redundancy. Experimental results show its effectiveness on high dimensional data.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bitanu Chatterjee, Trinav Bhattacharyya, Kushal Kanti Ghosh, Agneet Chatterjee, Ram Sarkar
Summary: This article presents a framework for maximizing influence propagation in a social network, which includes community detection and the utilization of the Shuffled Frog Leaping algorithm. Experimental results show that our method performs well compared to other algorithms.
Article
Computer Science, Information Systems
Soham Chattopadhyay, Arijit Dey, Pawan Kumar Singh, Ali Ahmadian, Ram Sarkar
Summary: Speech is crucial in human communication and human-computer interaction. In the field of AI and ML, it has been extensively studied to recognize human emotions from speech signals. To address the challenge of large feature dimension, a hybrid feature selection algorithm called CEOAS is proposed. By extracting LPC and LPCC features, the proposed model reduces feature dimension and improves classification accuracy. Impressive recognition accuracies have been achieved on four benchmark datasets, surpassing state-of-the-art algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ashis Paul, Arpan Basu, Mufti Mahmud, M. Shamim Kaiser, Ram Sarkar
Summary: This article discusses the use of deep learning models and an inverted bell-curve weighted ensemble method to assist in the detection of COVID-19 in CXR images. By using transfer learning and retraining models pretrained on the ImageNet dataset, as well as performing weighted average predictions, the accuracy of COVID-19 identification in CXR images can be improved.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Samir Malakar, Samanway Sahoo, Anuran Chakraborty, Ram Sarkar, Mita Nasipuri
Summary: Handwritten word recognition is an open research problem due to variations in writing style and degraded images. This paper proposes a holistic approach combined with distance calculation and feature descriptors to address the problem. The experimental results demonstrate the effectiveness of the proposed method on standard databases compared to deep learning models.
Article
Computer Science, Artificial Intelligence
Souradeep Mukhopadhyay, Sabbir Hossain, Samir Malakar, Erik Cuevas, Ram Sarkar
Summary: This paper introduces a new gray-scale contrast enhancement algorithm, which improves image quality by calculating near-optimal values using the Artificial Electric Field Algorithm (AEFA). Through comparisons with other techniques using standard metrics, simulation results show that the proposed method increases image contrast and enriches image information.
Article
Computer Science, Information Systems
Anubhab Das, Arka Choudhuri, Arpan Basu, Ram Sarkar
Summary: This study proposes a GAN-based method for generating handwritten Bengali compound characters to address data scarcity. The model's performance is evaluated by assessing the quality of generated samples, showing that it outperforms basic AC-GAN architecture and some other existing GAN architectures.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Apu Sarkar, S. K. Sabbir Hossain, Ram Sarkar
Summary: This paper proposes a method for human activity recognition (HAR) from wearable sensor data. It utilizes Continuous Wavelet Transform and a Spatial Attention-aided Convolutional Neural Network (CNN) to extract features, and employs feature selection and a modified version of Genetic Algorithm (GA) for activity recognition. Experimental results show that the proposed method outperforms existing models in terms of classification performance and improves overall recognition accuracy by reducing the number of features.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sagnik Ganguly, Sanmit Mandal, Samir Malakar, Ram Sarkar
Summary: This paper introduces a new copy-move image forgery detection technique which relies on a texture feature descriptor called Local Tetra Pattern (LTrP) for block level image comparison used to localize tampered region(s). Experimental results demonstrate that the proposed technique has been able to detect the forged regions with higher accuracy as compared to many state-of-the-art copy-move forgery detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Payel Pramanik, Souradeep Mukhopadhyay, Seyedali Mirjalili, Ram Sarkar
Summary: Breast cancer is a common malignancy in women, and early detection is crucial. In this research, a method for classifying breast masses using mammograms is proposed. Deep features are extracted using the VGG16 model with an attention mechanism, and an optimal features subset is obtained using a meta-heuristic algorithm. The proposed model shows successful identification and differentiation of malignant and healthy breasts.
NEURAL COMPUTING & APPLICATIONS
(2023)
Correction
Computer Science, Artificial Intelligence
Apu Sarkar, S. K. Sabbir Hossain, Ram Sarkar
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sk Mohiuddin, Khalid Hassan Sheikh, Samir Malakar, Juan D. Velasquez, Ram Sarkar
Summary: Digital face manipulation has become a significant concern recently due to its harmful effects on society, particularly for high-profile celebrities who can easily be targeted using apps like FaceSwap and FaceApp. Detecting deepfake images or videos is challenging, and existing models often fail to check for irrelevant or redundant features. In this study, a hierarchical feature selection (HFS) method using a hybrid population-based meta-heuristic model and a single solution-based meta-heuristic model was proposed. The model achieved high AUC scores on three publicly available datasets and outperformed most state-of-the-art methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Medicine, General & Internal
Arnab Bagchi, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a deadly disease that affects women worldwide. Early diagnosis and proper treatment can save lives. Breast image analysis, including histopathological image analysis, and computer-aided diagnosis, can help improve efficiency and accuracy in breast cancer detection. In this study, a deep learning-based method was developed to classify breast cancer using histopathological images, achieving high classification accuracy.
Article
Engineering, Biomedical
Agnish Bhattacharya, Biswajit Saha, Soham Chattopadhyay, Ram Sarkar
Summary: Cancer is a frightening disease that is extensively researched worldwide. This study presents a framework that utilizes deep learning and meta-heuristic approaches to accurately predict colon or lung cancer from histopathological images. By combining these methods, the proposed approach achieves near-perfect precision in cancer detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Automation & Control Systems
Debjit Sarkar, Sourodeep Roy, Samir Malakar, Ram Sarkar
Summary: Graph neural networks (GNN) maintain the essence of irregularly structured information in a graph through message passing and feature aggregation. A weighting scheme called VecGNN is proposed to incorporate inter-node feature-level correlational information, considering the relative position of nodes in the feature space. VecGNN outperforms baseline models GCN, GAT, and JKNets by 2%-4% on citation datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Ritam Guha, Kushal Kanti Ghosh, Suman Kumar Bera, Ram Sarkar, Seyedali Mirjalili
Summary: This paper proposes a binary adaptation of Equilibrium Optimizer (EO) called Discrete EO (DEO) for solving binary optimization problems. DEOSA algorithm, combining DEO with Simulated Annealing (SA) as a local search procedure, is applied to various datasets and outperforms other algorithms. The scalability and robustness of DEOSA are also tested on high-dimensional Microarray datasets and Knapsack problems, showing its superiority.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)