Special issue on International conference on computing and communication networks (<scp>ICCCN2022</scp>)

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