Drug-drug interactions (DDIs) pose a major concern in polypharmacy due to their potential to cause unexpected side effects that can adversely affect a patient's health. Therefore, it is crucial to identify DDIs effectively during the early stages of drug discovery and development. In this paper, a novel DDI prediction network (DDINet) is proposed to enhance the predictive performance over conventional DDI methods. Leveraging the DrugBank dataset, drugs are represented using the Simplified Molecular Input Line-Entry System (SMILES), with the RDKit software pre-processing the SMILES strings into their canonical forms. Multiple molecular fingerprinting techniques such as Extended Connectivity Fingerprints (ECFPs), Molecular ACCess System keys (MACCSkeys), PubChem Fingerprints, 3D molecular fingerprints (3D-FP), and molecular dynamics fingerprints (MDFPs) are employed to encode drug chemical structures into feature vectors. Drug similarities are computed using the Tanimoto coefficient (TC), and the final Structural Similarity Profile (SSP) is obtained by averaging the five molecular fingerprint types. The novelty of the approach lies in the integration of a Multi-head Attention centered Weighted Autoencoder (Mul_WAE) as the interaction prediction module, which leverages the Multi-head Attention (MHA) layer to focus on the most significant input features. Furthermore, we introduce the Upgraded Bald Eagle Search Optimization (UBesO) algorithm, which optimally selects the learnable parameters of the Mul_WAE based on cross-entropy loss, improving the model's convergence and performance. The proposed DDINet model achieves an accuracy of 99.77%, 99.66% of AUC, 99.5% average precision, 99.4% precision, and 99.49% recall, providing a comprehensive evaluation of the model's robustness. Beyond high accuracy, DDINet offers advantages in scalability, making it well suited for handling large datasets due to its efficient feature extraction and optimization processes. The unique combination of multiple molecular fingerprinting methods with the MHA layer and UBesO algorithm highlights the innovative aspects of our model and significantly improves prediction performance compared to existing approaches.

Drug-drug interactions (DDIs) are a significant issue in drug discovery, impacting research efficiency and patient safety. Precise prediction of DDIs is important, particularly when drugs are co-administered. The combination of heterogeneous data sources that reflect drug relationships and properties can greatly enhance predictive accuracy. This paper proposes a new Capsule-enclosed Coordinate Attention-based Dual Batch Depthwise Convolutional Knowledge Distillation (CC-DBDKD) model for DDI prediction. The input data drawn from the DrugBank dataset is preprocessed with the RDKit to standardize SMILES strings into their canonical representations. Various techniques of molecular fingerprint generation, such as Extended Connectivity Fingerprints, MACCS keys, PubChem Fingerprints, 3D molecular fingerprints, and molecular dynamics fingerprints, are used to map drug chemical structures onto feature vectors. Drug similarities are subsequently calculated by the Tanimoto coefficient, and the Structural Similarity Profile (SSP) is calculated as an average of these fingerprint types. A lightweight model, CC-DBDKD, improves DDI prediction by introducing capsule networks to learn spatial hierarchies and complex drug relationships. Coordinate attention mechanisms improve feature extraction by attending to key interaction patterns. Adding dual-batch depthwise convolutional layers improves computational efficiency to support scalability with large datasets. In addition, knowledge distillation reinforces the model by mapping knowledge from a teacher model to a student model, enhancing accuracy and robustness. The proposed model realizes superior accuracy values of 0.987 and 0.989 and an F1-score of 0.986, which outshines other prevailing models like CNN, CNN-LSTM, Autoencoder, and D-CNN. The outcomes position the CC-DBDKD model as a strong and scalable instrument for accurate DDI prediction.

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug-drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)-deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

A wireless sensor network (WSN) has a large number of sensor nodes and is utilized to gather data and transfer it via the shortest way to the base station. A transceiver was built into each sensor node so that it could send and receive data to nearby base stations. Secured data transmission in WSN is a significant one for efficient attack detection. Attack detection identified and categorized the attacks in wireless networks through matching the predefined patterns. Attack detection is an essential one to identify the attacks and to avoid the threat immediately for minimizing the attack impacts. Sinkhole, black hole, grey hole, wormhole and sybil attacks are various types of attacks. Different researchers carried out their research on attack detection in WSN. But, the attack detection accuracy was not increased and time consumption was not minimized. Machine learning and deep learning strategies are described for effective attack detection in WSN to overcome the issues.

Nature inspires human beings to a greater extent as the mother nature has guided us to solve many complex problems around us. Algorithms are developed by analysing the behaviour of the nature and from the working of groups of social agents like ants, bees, and insects. An algorithm developed based on this is called 'nature inspired algorithms'. These nature-inspired algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. A few algorithms are effective and have proved to be very efficient and thus have become popular tools for solving real-world problems. Swarm intelligence is one of the most important algorithms developed from the inspiration of group of habitats. The purpose of this paper is to present a list of comprehensive collective algorithms that invoke the research scope in that area.

The growing prominence of solar energy in decentralized renewable energy landscapes underscores the need for efficient solar energy trading mechanisms and seamless grid integration. This decentralized renewable energy sources, especially solar energy, present a significant prospect for the production of sustainable energy. This investigation explores the use of blockchain technology as a basic framework to tackle the problems associated with grid integration and solar energy trading in a decentralized setting. It uses the built-in advantages of blockchain technology, such as trust, transparency, and smart contract automation, to address the design and execution of a platform tailored for solar energy. As a result, the system efficiency increased to 93% and the transaction confirmation time was cut from 4.5 to 2.5 s with the lowest possible transaction cost. Furthermore, this study intends to offer insightful information about the technological viability, security, and scalability of blockchain solutions in the field of solar energy, while highlighting the significance of indicators of performance for an in-depth evaluation. This novel strategy is a crucial development for the field of green energy as it promises to revolutionize the way solar energy is traded, used, and integrated into modern grids.

Internet of Things (IoT) is an advanced applied science in recent years that enables communication among humans and smart components or among Internet-based components. Besides, IoT provides affiliation of physical and virtual elements that are fully controlled by different kinds of hardware, software, and interaction advancements. Though numerous methods of IoT offer many advantages to our day-to-day routine, it also possesses huge security weaknesses. The traditional methods are vulnerable to a huge range of attacks. Hence, establishing safe and effective security alternatives for the IoT environment remains as a main challenge and the major risk in this security solution is to transfer significant information in a secure manner. To address such limitations, an effective authentication approach named CHEK-based authentication methodology is devised for secure communication in IoT. The proposed authentication scheme consists of four steps and the authentication approach is developed by considering various security operations like hashing, encryption, secret key pairs, passwords, and so on. Moreover, the CHEK-based authentication scheme provides promising solution with minimum computational cost of 16.541 and minimum memory usage of 72.3MB.

An ad-hoc sensor network (ASN) is a group of sensing nodes that transmit data over a wireless link to a target node, direct or indirect, through a series of nodes. ASN becomes a high-risk group for several security exploits due to the sensor node's limited resources. Internal threats are more challenging to protect against than external attacks. The nodes are grouped, and calculate each node's trust level. The trust level is the result of combining internal and external trust degrees. Cluster heads (CH) are chosen based on the anticipated trust levels. The communications are then digitally signed by the source, encoded using a key pair given by a trustworthy CH, decoded by the recipient, and supervised by verifications. It authenticates the technique by identifying the presence of both the transmitter and the recipient. Our approach looks for a trustworthy neighboring node that meets the trust threshold condition to authenticate the key produced. The companion node reaffirms the node's reliability by getting the public-key certification. The seeking sensor node and the certification issuer node must have a close and trusting relationship. The results of the proposed hybrid authentication using a node trustworthy (HANT) system are modeled and tested, and the suggested approach outperforms conventional trust-based approaches in throughput, latency, lifetime, and vulnerability methods.

Classification is a crucial component of Computer Aided Diagnosis (CADx) systems. This phase comprises the extraction of features. Deep features have emerged as a new topic of study in numerous disciplines, including medical imaging. However, these works contain flaws, such as excessive classification, and do not reflect the real world. This paper provides an overview of deep learning for detecting lung illness in medical photos. In the past five years, just one review article has been published on deep learning for lung illness diagnosis. We investigate utilizing deep learning to detect and categorize Convolution neural network (CNN) numerous lung illnesses from chest X-ray images. We developed a pipeline for segmenting chest X-ray (CXR) images before classification and compared the performance of our framework to that of existing techniques. To recover lung characteristics, the Binary Spotted Hyena optimizer (BSHO) was used in this study. We demonstrated that simple models and classifiers, such as shallow CNN, can compete with complex systems. Furthermore, we validated our method using publicly available lung datasets from Shenzhen and Montgomery and compared its efficacy to that of existing methods. Despite having fewer trainable parameters, our technique outperformed the top performing models trained on the Montgomery dataset in terms of accuracy. In addition, although being computationally cheaper, our CNN-BSHO model performed nearly as well as the top solution on the Shenzhen dataset. This research employed four classifiers, including Support Vector Machine (SVM), Nave Bayes, Random Forest, and Visual Geometry Group (VGG). Using CNN-BSHO, an accuracy of 98.324% was reached.

Drug-Drug Interaction (DDI) is a significant challenge in modern healthcare as they have the potential to cause adverse side effects and hinder patient well-being. Accurate DDI prediction is critical for ensuring the efficacy and safety of medication management. This article proposes a novel method for detecting DDIs that makes use of convolutional neural networks (CNNs) for performing feature extraction and prediction augmentation. CNNs are used to automatically extract useful characteristics from drug combinations by using the underlying data patterns within drug interaction datasets. These collected features are then incorporated into a prediction model, which allows for more accurate detection of possible DDIs. To compare the performance of the proposed method, traditional classifiers used for drug-drug prediction, such as support vector machines (SVM), adaptive boosting (AB), and gradient boosting decision tree (GBDT), were preferred. The simulation results clearly demonstrated the efficacy of the proposed approach, emphasizing its potential for significantly enhancing the prediction accuracy. Notably, when multiple characteristics were interacted, the proposed strategy improved drug feature extraction by 24.4% when compared to the utilization of single features. This improvement indicates the method's robustness and capacity to capture complex interactions between pharmacological features, resulting in more accurate predictions.

The Hand gesture recognition-based research field plays a prominent role in the automated transformation of sign language and is the major source of communication among deaf people. During a reorganization of hand gestures, the background deduction cannot deal with sudden, drastic lighting changes leading to several inconsistencies. This method also requires relatively many parameters, which need to be selected intelligently. A novel Gabor Line Derivative Deep Convolution Neural Network-based Levy flight Whale optimization is introduced. Primarily pre-processing is done to diminish the computation complexity of processing red, green, and blue channel images. With the Gabor Line Derivative-based feature extraction technique, a relevant set of line features are extracted and subjected to the proposed optimization-driven deep learning algorithm. Deep learning approaches are quite popular in the recognition of HGIs, but choosing appropriate hyper-parameters is a complex problem. Additionally, the key problem associated with deep learning techniques is that the outcome of the accuracy measure attained is not much effective in the existing models. Thus, a novel Deep Convolution Neural Network based Levy flight Whale optimization is introduced in terms of categorizing dissimilar static and dynamic HGIs. The experimental analysis reveals that the proposed classifier performs better than other competitive existing methods through the performance matrices such as Precision, Accuracy, F1-score, Recall, specificity, Recognition time, FNR, FDR, loss, FPR, MCC, training time, and NPV. The combination of the proposed methods is enabled and attained an accuracy of about 97%. The implementation of this work is done in the python platform.

In the modern era, from big apartments to small houses, startups to corporate buildings, protecting assets or preventing unauthorized persons are crucial problems. Often traditional locks like padlocks are prone to security risks since they can be easily bypassed. Existing smart lock systems are prone to Man in middle Attacks where digital keys can easily be duplicated. The review comments about prevailing smart locks technologies have been collected from various sources such as blogs and microblogs. The data set is analyzed to discover the opinion of the people about the smartlock product. In this proposed system, an innovative smartlock system prototype is designed using current technologies. A smart lock system has been proposed which is encrypted end-to-end using the RSA algorithm. This system uses a one-time password sent to registered users combined with the master code to unlock the door. This system is designed as such only the users who are connected to a wireless local area network are able to access the smart lock system, this adds an additional layer of security. It is connected to the cloud and logs all the activity from booting to shutting down. The breach detection system along with image capture is also included to detect forced intrusions. The client functionality can be easily ported to any platform which supports HTTP protocol which tends to be the major advantage of the proposed work.

Drug-drug interactions (DDIs) pose significant challenges in the field of pharmaceutical research and clinical practice. Accurate prediction of potential DDIs is crucial to ensure patient safety and optimize treatment outcomes. In this study, an improved approach to enhance DDI prediction using a neural network framework (named as DDI-USNN) integrated with drug similarity measures has been developed. The proposed approach leverages comprehensive datasets containing drug pairs and their interaction labels to develop a robust predictive model. By incorporating drug similarity into the neural network architecture, the proposed approach captures subtle yet vital patterns underlying DDIs. The performance measures have been evaluated in terms of precision, recall and F-measure. Traditional classifiers commonly used for drug-drug prediction, including support vector machines (SVM), neural network based (NDD), adaptive boosting (AB), and gradient boosting decision tree (GBDT), were considered to compare the performance of proposed method by considering various datasets. Through meticulous experimentation and rigorous cross-validation, the simulation results unequivocally established the superior performance of the proposed integrated similarity-based neural network when compared to conventional methods and standalone neural networks. Notably, the DDI-USNN model achieved a substantial improvement in precision, outperforming AB by 32.7%, GBDT by 26.6%, NDD by 7%, and SVM by 5.5%. Furthermore, the recall values of each method were compared with the proposed DDI-USNN, revealing a marginal decrement of 1.6% in the recall value for DDI-USNN when compared to NDD. Additionally, the mean F-measure for the proposed DDI-USNN was computed and compared with existing methods, further underscoring its effectiveness in drug-drug interaction prediction.

Photovoltaic (PV) energy is becoming a more common way to produce clean, renewable energy. The PV modules with lengthy strings will be affected for the shading effects, resulting in a huge decrease in the final output power. To counter this, distributed maximum power point monitoring has been suggested, in which individual DC-DC converters are connected to a central DC-DC converter in which each PV module to get the most power out of it. To achieve the opti-mum power output, Distributed MPPT (DMPPT) is used and it compensates for shading effects and module mismatching issues.

In this day and age, technology and its functionalities are changing the way the world functions on a day-to-day basis, not only towards the betterment of this world but also towards the betterment of its inhabitants. This system is proposed with the intention of equipping athletes from underprivileged backgrounds with low-cost performance monitoring devices which would help them to monitor and analyse their performance, as well as to take the necessary future course of action. The proposed system makes use of an IoT-based low-cost WSN—Particle Argon which collects and stores the data in the Particle Cloud which can be used for real-time analysis as well as to predict the future course of action with the help of predictive analysis algorithm—linear regression. The proposed athlete monitoring system using predictive analysis algorithm (AMSPLA) is used to screen the basic health parameters of the patient, such as heart pulse rate and blood oxygen level as well as acceleration. These parameters are used to indicate the changes caused due the effort undertaken by the athlete. Furthermore, a report could be generated on the basis of the athlete’s performance, which can be used by their coaches to analyse their performance and also to facilitate future course of action.

Geopolymers are a new generation of inorganic polymers finding large potential in infrastructure applications. The present research article deals with the manufacture of innovative alkali activated bricks from municipal incinerated (MI) ash. The eco-sustainable alkali activated bricks are produced with fly ash and municipal incinerated ash as precursor materials with alkali activators such as sodium hydroxide and sodium silicate. A comprehensive study has been performed to investigate the suitability of MI ash for geopolymer brick production. The mineralogical characterizations of the brick samples were performed using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray powder diffraction (XRD) techniques. The mechanical and durability performance of the innovative alkaline activated bricks are explored, and the results demonstrated that the possibility of using MI ash up to 40% contributed to good mechanical and durability properties in the bricks. The geopolymer brick from MI ash paves a way for a greener building product by abating their disposal problem and reducing carbon footprint remarkably.

The stock market is the act of buying and selling the share of the companies and yield more profits. In order to provide the abnormal returns for the company by share market, the prediction of the stock market is necessary. Using deep learning algorithms, this analytics process predicts the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY). The deep learning algorithms such as simple recurrent neural network and long short-term memory algorithm are applied to predict the daily direction of future price of S&P 500 based on the historical price. The performance of different procedures such as simple RNN and LSTM is compared. LSTM algorithm is found to be an accurate algorithm when compared to the other algorithms. The simple RNN and LSTM algorithm is implemented on another dataset such as Bombay Stock Exchange (BSE), and the performance of both algorithms is compared and simulated; the result of LSTM is a better algorithm, to predict a stock market daily return.

The quick deployment of cloud with computing platforms has driven novel tendencies which shifted operations of networks. However, the cloud is facing several security issues and is susceptible because of suspicious tasks and attacks. This paper devises a new method to detect malicious activities in cloud. Here, first step is the simulation of cloud patterns, wherein the data outsourced by the users are utilized for detecting malicious behaviors. The data pre-processing is done to eradicate unnecessary data and noise contained in the data and is performed using a min–max normalization process. The selection of imperative features is done using distance measure, namely Hellinger distance for mining the essential features. The augmentation of data is performed to make the data appropriate for improved processing. The malicious behavior detection is performed by exploiting the Deep Q network wherein training is performed with Autoregressive chimp optimization algorithm (AChOA), which is developed by integrating chimp optimization algorithm (ChOA) and Conditional Autoregressive Value at risk (CAViaR). The proposed AChOA-based Deep Q network outperformed with the highest testing accuracy of 94

Background: Early hospital presentation is critical in the management of acute ischemic stroke. The effectiveness of stroke treatment is highly dependent on the amount of time lapsed between onset of symptoms and treatment. This study was aimed to identify the factors associated with prehospital delay in patients with acute stroke. Material and Methods: A cross-sectional descriptive study was conducted in Sri Ramachandra University Hospital, India. A total of 210 patients hospitalized in the stroke unit were included. Patients' data were obtained by interviewing the patient and/or accompanying family member and by reviewing their medical records using a standard questionnaire. Associations were determined between prehospital delay (≥4.5 h) and variables of interest by using univariate and multivariate logistic regression analyses. Results: The prehospital delay was observed in 154 patients (73.3%) and the median prehospital delay was 11.30 h. The following are the factors significantly (P < 0.05) attributed for the delay in presenting to the hospital: contextual factors like using public transport (bus), taxi, time of onset of symptoms, 7 pm–3 am; family history of stroke, perceived cognitive and behavioral factors like, wishing or praying for the symptoms to subside on its own, hesitation to travel due to long distance, delay in arranging transport, and arranging money for admission and wasting time by shopping for general practitioners, nursing homes, and hospitals. The presence of stroke symptom, headache, significantly decreased the prehospital delay. Conclusions: Prehospital delay is high in South India and influenced by clinical, contextual, and cognitive/behavioral factors.