Special issue on International conference on computing and communication networks (ICCCN2022) DOI Open Access
Deepak Gupta

Expert Systems, Journal Year: 2023, Volume and Issue: 41(7)

Published: Nov. 2, 2023

The past years have seen a flurry of activity in distributed computing and its related fields. Indeed, developments computer communications networks enabled the deployment exciting new areas, including internet things, vehicular networks, collaborative big data analysis so on. design implementation energy efficient future generation communication networking technologies fostered development mobile, pervasive large-scale technologies. International Conference on Computing Communications Networks (ICCCN 2022) aims to serve as forum for exchanging latest findings experiences ranging from theoretical research practical system all aspects networking. All submitted papers must substantial knowledge engineering component like AI/machine learning, etc. be scope this special issue, otherwise paper will get rejected straightaway. This issue brings selected presented at ICCCN 2022 conference. From around 45 articles, only 13 were based reviews. Each was reviewed by least two reviewers went through rounds brief contributions these are discussed below. In first authors Minni Jain et al. (2023) proposed approach code-mixed Hindi–English social media text that comprises language identification, detection correction non-word (out vocabulary) errors well real-word occurring simultaneously. A fuzzy graph between different words suggestive lists is generated using various semantic relations Hindi WordNet. Word embeddings graph-based centrality measures used find correct word. Several experiments performed datasets taken Instagram, Twitter, YouTube comments, Blogs WhatsApp. experimental results demonstrate corrects out-of-vocabulary with maximum recall 0.90 0.67, respectively, Dev_Hindi 0.87 0.66, Rom_Hindi. Ritu Bibyan their study prediction model LDA content aspect emotion sentiment aspect. validated collected Eclipse project convolutional neural network (CNN). show CNN effectively utilizes handle severity prediction. It also improves generalization overfitting avoided. Anshika Arora, Chakraborty, (2023), present novel methodology assessment objective sleep quality actigraph recordings motor activity. High level features sequential extracted Long-Short Term Memory (LSTM) model, which then paired significant statistical feature, namely zero percent, describes percentage events over series. predictive ability combined feature vector evaluated support machine (SVM) classifier. hybrid LSTM-SVM framework benchmark dataset, namely, MESA Actigraphy achieves an accuracy 85.62% concept overlapping sliding window performance 3.51%, use discriminative task 2.95%. Comparison state art validates indicator via actigraph-based data. next paper, Dimple Sethi gait markerless environment divided into three stages: First, database videos MNIT RAMAN LABORA-TORY Jaipur has been prepared. Second, skeletal landmark generate kinematic characteristics comparable gold standard marker-based techniques adapted. Third, MF-GA (multi-feature analysis), ensemble + LSTM, classification, developed. indicate can successfully low-cost clinical monitoring constraint-free environment. Our quantifying improve access quantitative clinics rehabilitation centres enable researchers conduct studies gait-related disorders. another research, Awanish Kumar Mishra introduced automated Cervical Precancerous Lesion Classification Quantum Invasive Weed Optimization Deep Learning (CPLC-QIWODL) biomedical pap smear images. CPLC-QIWODL technique examines images cervical cancer classification. Moreover, uses deep network-based SqueezeNet extraction. Furthermore, hyperparameter tuning takes place QIWO technique, showing novelty work. Finally, classify CC, variational autoencoder (DVAE) applied. result tested other existing algorithms, medical image database. Extensive comparative demonstrated enhanced outcomes 99.07%. Qiming Zhang marine radar internet-of-things make full echo AIS information target detection, radars work both scanning staring modes. By adopting segmentation learning methods, accurate perception weak plan-position (PPI) case X-band shows achieve high identification accuracy. Minzhi He studied application computed tomography (CT) intelligent algorithm recovery after surgery (ERAS) patients laparoscopic-assisted distal gastrectomy. An established collect lymph node metastasis data, groups compared. showed 54 cases predicted images, actual incidence 65.12%. PPS Bedi employed NLP-based algorithms summary perform linguistic summarization modify/adapt domain-specific summarization. provides developed in-house condensing ill-punctuated or unpunctuated discussion transcripts more intelligible summaries, combines topic modelling phrase selection punctuation restorations. For autonomous synthesis reports transcripts, proposes end-to-end Dense Long Short Network (LSTM), followed reveal models trained ordinary provide testing set, one outperforming test set. emphasizes popular soft computing, meta-heuristic named genetic (GA) solve optimization problem software system. Rajat Tandon, presents solution GA tool MATLAB. obtained relevance industry finding optimal values variables before release newly products ultimate client. diverse applications engineering, hardware industry. article Anurag Choudhary fault diagnosis method improved functionality (CNN) nature-inspired Artificial Bee Colony (ABCO) algorithm. diagnostic introduces analyses possible mechanical electrical faults IM. indicates infrared thermography-based anomaly outperforms vibration acoustic-based 100% classification signify potential diagnose IM reliability robustness. Subhashree Rout called Siamese-HYNAA, Siamese population-based optimizer hypercube natural aggregation (HYNAA) candidate solutions augmenting minority class. strategy against basic SMOTE SMOTE-PSOEV, measures, ROC-AUC curves, sensitivity, specificity, accuracy, characteristic stability index, balanced F1-score, informedness, markedness execution time. Siamese-HYNAA generates promising imbalanced Dongmei Jin analyse value magnetic resonance imaging Fuzzy C-means (FCM) neonatal hypoglycaemia brain injury (HBI), explore risk factors occurrence children, guidance treatment. average minimum blood glucose (1.09 ± 0.53 mmoL/L) group lower than non-brain (1.75 0.49 mmoL/L), duration (43.1 21.07 h) higher (13.79 6.81 h), p < 0.05. conclusion, MRI FCM clustering had quality. Late feeding time, low sugar long hypoglycaemic injury. final Mahmoud Ragab facilitate problems learning. binary particle swarm (PSO) firefly (FA) such it blends best each optimized way solving said problem. suggested assessed six domains publicly available UCI repository validity. similar evolutionary-based approaches prove superiority. PSO found suitable problems. To conclude, publishes out total papers. guest editors hope would benefit readers. We want express our sincere thanks editor-in-chief allowing us organize particular issue. editorial office staffs excellent, we thank them support. thankful who made possible, thoughtful contributions.

Language: Английский

Reinforcement learning guided auto-select optimization algorithm for feature selection DOI
Hongbo Zhang, Xiaofeng Yue,

Xueliang Gao

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 268, P. 126320 - 126320

Published: Jan. 5, 2025

Language: Английский

Citations

1

Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection DOI Creative Commons

Reham Amin,

Ghada Eltaweel,

Ahmed F. Ali

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 93235 - 93260

Published: Jan. 1, 2024

Cyber Threat Detection (CTD) is subject to complicated and rapidly accelerating developments. Poor accuracy, high learning complexity, limited scalability, a false positive rate are problems that CTD encounters. Deep Learning defense mechanisms aim build effective models for threat detection protection allowing them adapt the complex ever-accelerating changes in field of CTD. Furthermore, swarm intelligence algorithms have been developed tackle optimization challenges. In this paper, Chaotic Zebra Optimization Long-Short Term Memory (CZOLSTM) algorithm proposed. The proposed hybrid between Algorithm (CZOA) feature selection LSTM cyber classification CSE-CIC-IDS2018 dataset. Invoking chaotic map CZOLSTM can improve diversity search avoid trapping local minimum. evaluating effectiveness newly CZOLSTM, binary multi-class classifications considered. acquired outcomes demonstrate efficiency implemented improvements across many other algorithms. When comparing performance detection, it outperforms six innovative deep five classification. Other evaluation criteria such as recall, F1 score, precision also used comparison. results showed best accuracy was achieved using 99.83%, with F1-score 99.82%, recall 99.82%. among compared

Language: Английский

Citations

5

Fitness and historical success information-assisted binary particle swarm optimization for feature selection DOI
Shubham Gupta, Saurabh Gupta

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 306, P. 112699 - 112699

Published: Nov. 10, 2024

Language: Английский

Citations

4

Improved Salp Swarm Optimization Algorithm based on a Robust Search Strategy and a Novel Local Search Algorithm for Feature Selection Problems DOI

Mahdieh Khorashadizade,

Elham Abbasi, Seyed Abolfazl Shahzadeh Fazeli

et al.

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2025, Volume and Issue: 258, P. 105343 - 105343

Published: Feb. 7, 2025

Language: Английский

Citations

0

Twin Q-learning-driven forest ecosystem optimization for feature selection DOI
Hongbo Zhang, Jinlong Li, Xiaofeng Yue

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113323 - 113323

Published: March 1, 2025

Language: Английский

Citations

0

Joint identification of groundwater contaminant sources: an improved optimization algorithm DOI

Zheng Guo,

Boyan Sun,

Saiju Li

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(5)

Published: April 5, 2025

Language: Английский

Citations

0

Automated detection of ChatGPT-generated text vs. human text using gannet-optimized deep learning DOI
Abdulrhman M. Alshareef, Aisha Alsobhi, Alaa O. Khadidos

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 124, P. 495 - 512

Published: April 11, 2025

Language: Английский

Citations

0

Probe mechanism based particle swarm optimization for feature selection DOI
Hongbo Zhang,

Xiwen Qin,

Xueliang Gao

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(6), P. 8393 - 8411

Published: April 10, 2024

Language: Английский

Citations

3

Mantis Search Algorithm Integrated with Opposition-Based Learning and Simulated Annealing for Feature Selection DOI Creative Commons

Samia Mandour,

Abduallah Gamal, Ahmed Sleem

et al.

Sustainable Machine Intelligence Journal, Journal Year: 2024, Volume and Issue: 8

Published: June 18, 2024

Feature selection (FS) plays a vital role in minimizing the high-dimensional data as much possible to aid enhancing classification accuracy and reducing computational costs. The purpose of FS techniques is extract most effective subset features, which might enable machine learning (ML) algorithms better grasp input data’s patterns improve their performance. Although several metaheuristic have been recently presented solve this problem, they still suffer from disadvantages, such getting stuck local optima, slow convergence speed, lack population diversity, prevent them achieving desired solutions an acceptable time. Therefore, study propose new feature approach, namely OBMSASA, based on integrating published mantis search algorithm with opposition-based (OBL) method simulated annealing (SA) strengthen its exploration exploitation operators. OBL aims operator, making able avoid stagnation into minima; meanwhile, SA used further thereby improving speed. K-nearest neighbor compute selected feature. proposed assessed using 21 common datasets compared rival optimizers terms performance metrics, including curve, average fitness, cost, length standard deviation, observe effectiveness efficiency. source code publicly accessible at https://drive.mathworks.com/OBMSASA.

Language: Английский

Citations

3

Mathematical modeling of a Hybrid Mutated Tunicate Swarm Algorithm for Feature Selection and Global Optimization DOI Creative Commons

Turki Althaqafi

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(9), P. 24336 - 24358

Published: Jan. 1, 2024

<p>The latest advances in engineering, science, and technology have contributed to an enormous generation of datasets. This vast dataset contains irrelevant, redundant, noisy features that adversely impact classification performance data mining machine learning (ML) techniques. Feature selection (FS) is a preprocessing stage minimize the dimensionality by choosing most prominent feature while improving performance. Since size produced are often extensive dimension, this enhances complexity search space, where maximal number potential solutions 2nd for n As becomes large, it computationally impossible compute feature. Therefore, there need effective FS techniques large-scale problems classification. Many metaheuristic approaches were utilized resolve challenges heuristic-based approaches. Recently, swarm algorithm has been suggested demonstrated perform effectively tasks. I developed Hybrid Mutated Tunicate Swarm Algorithm Global Optimization (HMTSA-FSGO) technique. The proposed HMTSA-FSGO model mainly aims eradicate unwanted choose relevant ones highly classifier results. In model, HMTSA derived integrating standard TSA with two concepts: A dynamic s-best mutation operator optimal trade-off between exploration exploitation directional rule enhanced space exploration. also includes bidirectional long short-term memory (BiLSTM) examine process. rat optimizer (RSO) can hyperparameters boost BiLSTM network simulation analysis technique tested using series experiments. investigational validation showed superior outcome 93.01%, 97.39%, 61.59%, 99.15%, 67.81% over diverse datasets.</p>

Language: Английский

Citations

2