In wireless networks, automatic modulation classification (AMC) is crucial for enabling intelligent signal demodulation, thereby enhancing the system’s adaptability across various applications. Concurrently, the rapid expansion of the Internet of Things (IoT) necessitates scalable network solutions with limited power consumption. Moreover, addressing the Nonline-of-Sight (NLoS) effects in IoT networks, intelligent reflecting surface (IRS) emerges as a promising, cost-effective technology. This article introduces a novel transformer-based deep recurrent architecture (TDRA) for AMC, tailored for IRS-assisted IoT networks, which significantly improves IoT Device (IoTD) performance in NLoS scenarios. In TDRA, the existing recurrent models, long-short-term memory (LSTM), and gated-recurrent-unit (GRU) are suitably revamped with a transformer-based approach and termed as transformer-based LSTM (T-LSTM) and transformer-based GRU (T-GRU). Numerical data sets are generated for IoT applications considering the seven widely used modulation types to train and test the proposed models. Comparative analysis with seven state-of-the-art deep learning models and five machine learning models for AMC demonstrates the superior performance of the proposed models across multiple metrics, including accuracy, R-squared-score, mean-square error, mean-absolute error, precision, recall, and F1-score. Further, the proposed models exhibit notable improvements under various conditions, such as optimized and random IRS phase shifts, with and without IRS-assisted IoT networks, different modulation sequence lengths, and fading channels. Additionally, the time complexity and processing time of the proposed models have been studied to test their suitability for IoTD. The simulation results indicate that the TDRA for AMC in IRS-assisted IoT networks achieves up to 87% higher accuracy compared to without IRS-assisted IoT networks. This significant enhancement underscores the potential of TDRA to revolutionize IoT networks by providing robust, efficient, and scalable solutions for real-world applications.

Abstract The robustness of wireless sensor networks (WSNs) has made it a preferred choice for many applications. WSNs should perform the intended sensing task with a limited amount of energy. Clustering methodology is used to reduce sensor nodes' energy consumption and increase the network lifetime by organizing nodes into independent groups. In this article, a hierarchical structured cluster routing (HSCR) protocol based on the principle of an m‐way balanced tree has been proposed for multi‐hop intra‐cluster communication and offload the cluster head. It enhances the network performance by providing an underlying architecture that reduces the intra‐cluster communication distance and the amount of data sent to the cluster head. In the proposed HSCR protocol, an m‐way tree‐based hierarchical structure has been proposed in which balanced structure is formed within each cluster so that data aggregation is performed at each intermediate parent node. The proposed approach can be implemented over any communication protocol used in WSNs. Simulation results prove that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, and network lifetime. Simulation results affirm that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, average intra‐cluster communication distance and network lifetime by 105.32%, 49.23%, 14.98%, 109.31%, and 35.75%, 15.32%, 17%, 24.31%, respectively.

The propagation environment was uncontrollable in first-generation to fifth-generation (5G) wireless technologies. This behavior of the wireless propagation environment is one of the prime constraints in harnessing the performance of wireless networks. This problem can be addressed in sixth-generation (6G) wireless networks by deploying intelligent reflecting surfaces (IRSs). IRS’s amplitude and phase reflecting coefficient of reflecting units (RUs) can be adjusted via a programmable controller to meet the network requirements. On the other hand, in 5G and 6G wireless communication networks, non-orthogonal multiple access (NOMA) is a robust and well-admired multiple access scheme among the other multiple access counterparts in terms of spectrum efficiency and link capacity. NOMA allows many user equipment (UE) by utilizing non-orthogonal distribution of resources. Therefore, the combination of IRS and NOMA is one of the dominant technologies for 6G wireless networks. Based upon the importance of NOMA and IRS in the initial development of 6G wireless networks, this paper presents a comprehensive survey on IRS-assisted NOMA-based networks, considering their designs and challenges. In this work, the concept and structure of IRS-assisted NOMA have been explained with an in-depth analysis of the frameworks. It also includes some challenges of IRS-assisted NOMA in wireless communication networks. Further, applications and future research directions of IRSassisted NOMA networks are discussed.

In recent times, software developers widely use instant messaging and collaboration platforms, as these platforms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually.Gitter is one such platform that has a heavy userbase.It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies.Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers.The classification techniques can be deployed for this purpose.The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques.In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevsrest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose.Further, two feature selection methods have been applied.The SMOTE technique has been used for handling data imbalance.It resulted in a total of 1836 classification pipelines for analysis.The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand.The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three topperforming word embeddings having performance values taken across different classification techniques.The models trained using ANOVA features performed similarly to those models trained using all features.Finally, using the SMOTE technique helps models to get a better prediction ability.

In silico machine learning based prediction of drug functions considering the drug properties would substantially enhance the speed and reduce the cost of identifying promising drug leads. The drug function prediction capability of different drug properties happens to be different. So assessing these is advantageous in drug discovery. The task of drug function prediction is multi-label in nature reason being, in case of several drugs, multiple functions are associated with a drug. A number of existing works have ignored this inherent multi-label nature of the problem in context of addressing the issue of class imbalance. In the present work, a computational framework named as BRMCF has been proposed for analysing the prediction capability of chemical and biological properties of drugs toward drug functions in view of multi-label nature of problem. It employs Binary Relevance (BR) approach along with five base classifiers for handling the multi-label prediction task and MLSMOTE for addressing the issue of class imbalance. The proposed framework has been validated and compared with BR, Classifier Chains (CC) and Deep Neural Network (DNN) method on four drug properties datasets: SMILES Strings (SS) dataset, 17 Molecular Descriptors (17MD) dataset, Protein Sequences (PS) dataset and drug perturbed Gene EXpression Profiles (GEX) dataset. The analysis of results shows that the proposed framework BRMCF has outperformed BR, CC and DNN method in terms of exact match ratio, precision, recall, F1-score, ROC-AUC which signifies the effectiveness of MLSMOTE. Further, assessment of prediction capability of different drug properties is done and they are ranked as SS GEX PS 17MD. Additionally, the visualization and analysis of drug function co-occurrences signify the appropriateness of the proposed framework for drug function co-occurrence detection and in signaling the new possible drug leads where the detection rate varies from 94.34% to 99.61%.

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert$\Gamma$statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.

Outlier detection is a very important task in many fields like network intrusion detection, credit card fraud detection, stock market analysis, detecting outlying cases in medical data etc. Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving in coming data over time. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data. In proposed scheme both density based and partitioning clustering method are combined to take advantage of both density based and distance based outlier detection. Proposed scheme also assigns weights to attributes depending upon their respective relevance in mining task and weights are adaptive in nature. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.