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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

Graphene oxide–silver (Ag–GO) nanocomposite has emerged as a vital antibacterial agent very recently. In this work, we report a facile one step route of Ag–GO nanocomposite formation excluding the aid of surfactants and reductants and was successfully applied to negative Escherichia Coli ( E coli ) to investigate antibacterial activity by varying doze concentration. The successful formation of Ag–GO nanocomposite via facile one step route was confirmed using Fourier transform infrared spectroscopy (FTIR) and Raman Spectroscopy. The absorption spectra (peak ~ 300 nm) for GO and the (peak ~ 420 nm) for silver nanoparticles were observed. XRD study confirmed the formation of Ag–GO nanocomposite while atomic force microscopy (AFM) showed crumbled GO sheets decorated with Ag nanoparticles. It was observed that the functional groups of GO facilitated the binding of Ag nanoparticles to GO network and enhanced the antibacterial activity of the nanocomposite.

Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a deep learning (DL) technique for the classification of arrhythmias. The proposed technique makes use of the University of California, Irvine (UCI) repository, which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes. In this research, our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset. The DL approach in the form of long short-term memory (LSTM) is an efficient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis. The goal of this research was to categorize cardiac arrhythmia patients by developing an efficient intelligent system using the LSTM DL algorithm. This approach to arrhythmia classification includes classification algorithms along with noise removal techniques. Therefore, we utilized principal components analysis (PCA) for noise removal, and LSTM for classification. This hybrid comprehensive arrhythmia classification approach performs better than previous approaches to arrhythmia classification. We attained a highest classification accuracy of 93.5% with the DL based disease classification system, and outperformed the earlier approaches used for cardiac arrhythmia classification.

The trend to shop secondhand products (SHPs) is accelerating owing to a substantial interest of customers particularly toward online shopping of SHPs. Drawing on the expectation–confirmation model (ECM), this study aims to examine the relationships among customer expectation, perceived enjoyment, perceived ease of use (PEOU), satisfaction, and repurchase intention of online shopping of used products. Data were collected using a convenience sampling technique from 400 Chinese shoppers often acquire used products online. The results revealed that expectation significantly affects perceived enjoyment, PEOU, and satisfaction. The findings further reported that perceived enjoyment has a positive influence on satisfaction and repurchase intention. Likewise, satisfaction has a positive direct effect on repurchase intention. Our results affirmed that satisfaction partially mediates the relationships among expectation, perceived enjoyment, and repurchase intention, whereas no mediation established among PEOU, satisfaction, and repurchase intention. Finally, an insignificant effect of PEOU on satisfaction and repurchase intention was observed. The study empirically furnishes insightful information for the organizations to offer SHPs online to enhance organizational profit. Theoretical and managerial implications along with research opportunities are reported.

Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop a hybrid intelligent intrusion detection system (HIIDS) to learn crucial features representation efficiently and automatically from massive unlabeled raw network traffic data. Many ID datasets are publicly available to the cybersecurity research community. As such, we used a spark MLlib (machine learning library)-based robust classifier, such as logistic regression (LR), extreme gradient boosting (XGB) was used for anomaly detection, and a state-of-the-art DL, such as a long short-term memory autoencoder (LSTMAE) for misuse attack was used to develop an efficient and HIIDS to detect and classify unpredictable attacks. Our approach utilized LSTM to detect temporal features and an AE to more efficiently detect global features. Therefore, to evaluate the efficacy of our proposed approach, experiments were conducted on a publicly existing dataset, the contemporary real-life ISCX-UNB dataset. The simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS significantly outperformed existing ID approaches, achieving a high accuracy rate of up to 97.52% for the ISCX-UNB dataset respectively 10-fold cross-validation test. It is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.

PurposeThe aims of this study were three-fold: to determine the impact of green transformational leadership on creative process engagement, green product innovation and green process innovation; to examine the association of creative process engagement with green product and process innovation and to identify the mediating influence of creative process engagement in the association between green transformational leadership and green process and product innovation.Design/methodology/approachData was collected through a survey questionnaire from 291 middle- and lower-level managers and employees through simple random sampling in four high-tech manufacturing industries situated in Beijing, Shanghai and Shenzhen in China. We examined the data through structural equation modeling using partial least squares to test the study hypotheses.FindingsThe findings unveiled that green transformational leadership and creative process engagement positively influence green product innovation and green process innovation. Similarly, green transformational leadership is positively linked with creative process engagement. The findings further revealed that creative process engagement mediates the impact of green transformational leadership on green process and product innovation. Hence, our findings provide strong support for the role of green transformational leadership and creative process engagement in improving green process and product innovation.Research limitations/implicationsOur sample is limited to China and collected from high-tech manufacturing industries.Practical implicationsDrawing on the componential theory of creativity, the authors suggest that organizational leaders, specifically those who practice green transformational leadership, should increase creative process engagement among subordinates, as it is a crucial intangible resource for green process and product innovation.Social implicationsWe suggest that a combination of green transformational leadership and creative process engagement improves green process and product innovation as well as the environmental performance of a business by eliminating all forms of hazardous material and waste.Originality/valueThis work is one of the earliest empirical studies to evaluate the influence of green transformational leadership on fostering green product and process innovation and the mediating impact of creative process engagement on the linkage among green transformational leadership, green product and process innovation within the manufacturing context.

Abiotic stresses are the prime coercion to sustainable crop production in changing climate scenario. Heat and drought stresses at reproductive as well as vegetative stages of rice cause extensive reduction in its yield. Being a multifunctional amino acid, proline is being used to diminish numerous biotic and abiotic stresses of plants. A pot experiment was conducted in summer season 2018, to check the effectiveness of foliar applied proline in mitigating the concurrent effects of heat and drought stresses on rice, at greenhouse/screenhouse of Faculty of Agriculture, University of Agriculture, Faisalabad, Pakistan. Experiment was carried out under completely randomized design with split arrangement having three replications. Stress was imposed at anthesis with treatments viz. drought stress, heat stress and heat plus drought stress as a main factor, and various levels of exogenously applied proline viz. no proline/water spray, 10, 20 and 30 mM concentrations were maintained as subfactor. A control (no stress imposed) was upheld for comparison. Stress treatments: drought, heat and heat plus drought stress at anthesis stage depressed the production of antioxidants, osmoprotectants and chlorophyll contents while causing overproduction of malondialdehyde content. Exogenous proline application upregulated activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), total soluble proteins (TSP), leaf proline and glycine betaine contents and diminution in lipid peroxidation resulting into improvement in chlorophyll contents and eventually yield per plant. Concurrent heat and drought stresses were more perilous as compared to individually applied heat or drought stress and 30 mM proline application gave maximum alleviation against stress.

Synthetic heterocyclic compounds have incredible potential against different diseases; pyridines, phenolic compounds and the derivatives of azo moiety have shown excellent antimicrobial, antiviral, antidiabetic, anti-melanogenic, anti-ulcer, anticancer, anti-mycobacterial, anti-inflammatory, DNA binding and chemosensing activities. In the present review, the above-mentioned activities of the nitrogen-containing heterocyclic compounds (pyridines), hydroxyl (phenols) and azo derivatives are discussed with reference to the minimum inhibitory concentration and structure–activity relationship, which clearly indicate that the presence of nitrogen in the phenyl ring; in addition, the hydroxyl substituent and the incorporation of a diazo group is crucial for the improved efficacies of the compounds in probing different diseases. The comparison was made with the reported drugs and new synthetic derivatives that showed recent therapeutic perspectives made in the last five years.

This study investigates the impacts of climate change on yield of selected cereal crops (wheat and maize) in the northern climatic region of Khyber Pakhtunkhwa (KP) province of Pakistan for the period 1986–2015. The first-generation unit root tests such as the Levin, Lin, and Chu (LLC), augmented Dickey-Fuller (ADF)–Fisher, and the second-generation unit root tests such as cross-sectional augmented Im-Pesaran-Shin (CIPS) and cross-sectional ADF (CADF) are used to check stationarity of the series. The cointegration among the variables is discovered via Pedroni test and Westerlund method. The long- and short-run impacts of climatic variables (average precipitation, maximum temperature, and minimum temperature) on yield of wheat and maize crops are assessed through the autoregressive distributed lag (ARDL) model. The empirical findings reveal that average precipitation has a significantly positive impact on yield of both crops in long- as well as short-run. The results further reveal that the effect of average minimum temperature on both crops is insignificant in long-run. However, the short-run effect of average minimum temperature is significantly positive on yield of maize crop but insignificant on yield of wheat crop. In long-run, an increase in average maximum temperature negatively affects crop yield. In short-run, however, it positively affects the yield of wheat and maize crops. The study recommends that increase in area under cultivation, development of advanced irrigation system, and farmers’ access to metrological information will help in lowering the drastic impacts of climate change on crop productivity.

Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among climatic, soil, and environmental factors. This study introduces DeepAgroNet, a novel three-branch deep learning framework that integrates satellite imagery, meteorological data, and soil characteristics to estimate winter wheat yields at the district level in southern Pakistan. The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. CNN emerged as the most effective model, achieving an R 2 value of 0.77 and a forecast accuracy of 98% one month before harvest. The RNN and ANN models also demonstrated moderate predictive capabilities, with R 2 values of 0.72 and 0.66, respectively. The results showed that all models achieved less than 10% yield error rates, highlighting their ability to effectively integrate spatial, temporal, and static data. This study emphasizes the importance of deep learning in addressing the limitations of traditional manual methods for yield prediction. By benchmarking the results against Crop Report Services data, this study confirms the reliability and scalability of the proposed framework. The findings demonstrate the potential of DeepAgroNet to improve precision agriculture practices, contributing to food security and sustainable agricultural development in Pakistan. Furthermore, this adaptable framework can serve as a model for similar applications in other agricultural regions around the world.