Background Childhood is the most essential phase in a person’s life as the foundations laid at this stage decide the future. Children are one of the vulnerable groups during any disaster. It is a stressful event that is not easily understood. It is emotionally confusing and frightening and results in children needing significant instrumental and emotional support from adults. Aim To understand the psychosocial impact of the COVID-19 pandemic on school-going adolescents. Methods This study adopted a cross-sectional research design in which sociodemographic characteristics and the CRIES-13 were administered to all the 312 children studying in 9th and 10th grade from the five selected schools. Statistical Analysis Descriptive statistics such as frequencies, mean, and percentages were employed to analyse participants’ demographic characteristics. Chi-square, independent t-test, and ANOVA were used for comparison of the data. Results The mean age of the respondents was 15 years (SD 0.98). It was found that most of the children (90.4%) had disturbances in their education, with 10th grade children having more disturbance than 9th grade ones (P value < 0.05). Further, intrusion and arousal were found to be significantly higher among male children than female children (P value < 0.05). Also, intrusion and arousal are significantly higher among 9th grade students, whereas avoidance is higher among 10th grade students (P value < 0.05). Conclusion The study identified that COVID-19 has a profound psychosocial impact on school going-adolescents. This finding highlights the importance of understanding the pandemic’s impact on children from a psychosocial perspective and designing appropriate interventions for them.

The information technology has grown so rapidly that it has led to the development of compact size and inexpensive sensor nodes. Several sensor nodes together form a WSN. Though the WSN is compact in size, they can be equipped with radio transceivers, sensors, microprocessors which are embedded and sensors. One of the major critical issues with WSN is energy efficiency. With WSN, various energy-efficient techniques are being employed. Among clustering techniques, LEACH, HEED, EAMMH, TEEN, SEP, DEEC, K-means clustering algorithm are some of the most popular energy-efficient techniques which are employed. These are hierarchical based protocol which saves energy by balancing the energy expense. Detailed review and analysis of these protocols are presented, and midpoint location algorithm is proposed in this paper. The methodology used for reduction in dead nodes while transmitting the data is also discussed. In the proposed work, path construction phase (PCP) and alternative path construction phase (APCP) are created in order to reduce dead nodes. During the processes of data transmission if a node is found out that it will fail and APCP is applied, the cluster head is changed while applying the APCP. The cluster head is chosen based on midpoint location and highest node energy. The cluster head becomes permanent if the node has midpoint location and the highest energy. If the node does not have midpoint location and highest energy, it becomes a temporary cluster head. The proposed techniques are compared with EAMMH protocol and LEACH protocol using MATLAB. When compared with EAMMH, the dead nodes were reduced with subsequent rounds.

The quantum key distribution (QKD) technique provided a promising resolution to the current security threats in Quantum Communication. However, the conventional QKD approach is vulnerable to hacker attacks and the Quantum particles used in QKD lose their energy during long-distance communication. To increase the distance coverage of quantum communication, our system proposed a Multi-layer Proxy Encryption Scheme (MPES) using entangled quantum particles. The main advantage of MPES over other conventional communication techniques is that each quantum repeater in the network acts as a different source of encryption with both sender and receiver nodes. For effective long-distance communication, this system adopted a trust-based short-distance protocol to find the path of photon transfer. The entangled photon that is used in communication is done through normal fibre optic cable. The presence of an eavesdropper can be measured with the help of an error correction protocol and a public key is sent through the normal communication channel. This pattern is checked throughout the communication path and malicious nodes that change its pattern will be eliminated from the network. This multiple encoding enhances the security level and reduces the end decryption time effectiveness. Quantum repeaters are used in the QKD protocol to extend the transmission distance by implementing quantum correlations. Unlike the conventional QR, our proposed structure performs one-way communication by encoding and decoding the data within a single node. The reader can decode the information from the actual sender and the writer transforms this decoded data to another node of quantum repeaters. The qubits that are decoded will be in a bell state, and qubits transfer the data in the form of polarization.Moreover, the shortest path algorithm-based photon transfer is done in this approach which increased the execution period of the proposed approach and also turned into the enhanced cost-effective technique. The obtained key error rate of the proposed system is compared with the conventional BB84 protocol and the comparison result proved an increase of 30% in error reduction and reduced energy consumption.

In sensor nodes the communications are established for many commercial purposes using basic client server method. The data is exposed to outside world while communicating with the physical environment. The data is not secured and needs to be protected from external threats. The idea behind this research work is to establish a secure smart wireless connection between a client and server. The proposed work is using TinyOS RTOS for implementing a smart and secure data as compared with GPRS method of transmission and reception. The main advantages in our method advantages are that the number of nodes can be increased, the visual method between the user and the system increases the flexibility of the system and also the interfacing circuit options are embedded as software models. We have implemented the application using IEEE 802.15.4 standard communication protocol. The zigbee is the most commonly used wireless device for all sensor networks. The zigbee assembling with PC needs external interface system for interfacing. The IoT and android based smart metering needs a coding method for data acquisition but tinyOS data acquisition is acquired by hardware. An encryption method is developed on transmission side to send the data securely. Hence the sensor MOTES receive the real time data and transmit to the remote nodes through wireless transmission. A primary key value is added with the sensor data and is transmitted from the gate way base station (server) to the remote sensor nodes (clients). The hardware is implemented in TinyOS platform using network embedded C (nesC) software. The added advantage is (over the air programming) OTAP of the MOTEWORKS software used in our proposed work. The special feature of TinyOS RTOS is that it has a better visualization tool for the end user to monitor the sensor nodes. The board data is monitored and viewed through the MOTEVIEW visualization tool. The method implements the encryption using the RTOS task in TinyOS MOTES. The proposed method occupies less memory space for task as compared to event based simulation. The data is finally transmitted as wireless sensor MOTES and controlled by the server.

The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks.

In recent days, cloud computing is a universal computing and conventional paradigm, in which the resources are provided over the Internet based on requirements. With the huge growth of cloud storage and processing, security in Cloud has become the most captivating research domains. Though there are many methods for enhancing security and data confidentiality over cloud, security is the major threat among data owners and users in data storages and data sharing between two parties in Cloud. In data sharing, for assuring security, the data are secured with key and the key is required to be encrypted to keep that not accessible for attacks. With that concern, this work develops a Quantum Cryptography based Cloud Security Model (QC-CSM) that uses Quantum Key Distribution Protocol (QKDP) for sharing the secret key between parties. For ensuring the data owner about the security of their share data over Cloud, Attribute Based Encryption (ABE) is used. Further, the data can be accessed by an authenticated user, who is having the access for decryption through the key from a secure quantum channel. The results show that the proposed model outperforms the existing works by providing a more secure environment and confidential data sharing between entities in minimal time in the Cloud framework.

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).

Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases. Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by “adaptive K‐means clustering.” This is also accomplished by the Fitness Sorted‐Shark Smell Optimization (FS‐SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS‐SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

Social media plays a vital role in the user community all over the globe. It makes it easy to communicate from one person to another person through these social media platforms. But these platforms are coming under various security issues and privacy of user-related data that make it tough to maintain. Test data generated from existing tools are used for analysis on these platforms. According to various roles in any real-world application, environment for the user, such as community deduction, analyzing user profiles, and preventing security threats, is performed on these data. In this paper, we have surveyed various factors related to security and privacy in a social network and listed various advantages and disadvantages of various approaches, thereby it acts as a base paper for future research work in the field of social networking.

The usage of pig iron sludge nanoparticles as adsorbent was explored to remove Cr(VI) from effluents. Batch sorption was administered to check the effect of various parameters on the sorption method, namely, pH, interaction time, as well as Cr(VI) concentration and dosage. The adsorption potential was related with the parameters. An inovative flow chart was proposed by using this sludge, which is a byproduct from the pig iron manufacturing industries, as adsorbent for the treatment of Cr(IV)-contaminated water, and then this sludge is recycled for drying followed by filtration. The adsorbed Cr(VI) will be useful for producing steel in a blast furnace. The sorption followed a second-order mechanism and the experimental data obeyed the Langmuir adsorption isotherm. Surface diffusion was found to be the sluggish step and thus is the rate-limiting step.

In today's scenario of computing paradigm, the cloud framework has become a significant solution on peak of virtualization for the utilization of computing models. However, the model has the latent to influence users and organizations; there are several security issues over shared data. In existing models for cloud data security, several considerations are made. Still, there is a requirement for ensuring cloud storage security with Third Party Auditing and distributed accountability. For that, this paper develops a new model called Enhanced Cloud Security Model using Quantum Key Distribution Protocol (ECSM-QKDP), for providing cloud storage security and manage with data dynamics, quantum key cryptography is incorporated. Moreover, this work considers the scenario of communication between three entities such as, Cloud Server, Data Owner and Legitimate User (LU), in which the quantum keys are shared in two steps. In the first step, BB84 QKDP is used and in the second process, Secure Authentication Protocol is framed based on distance bounding and secure keys, which are generated here using Hierarchical Attribute-Set based Encryption. By utilizing the model, the secured keys are transmitted through trusted channel to the LU. The results show that the proposed model provides effective results than existing models.

Sentiment analysis for user reviews has received substantial heed in recent years. There are many deep learning models for natural language processing (NLP) applications. Long-short term memory (LSTM) and Convolutional neural network (CNN) based models efficiently enhance sentiment accuracy. Aspect-level sentiment analysis involves aspect extraction, aspect categorization, and polarity classification. The aspect sentiments in the dataset are classified as positive, negative, and neutral, depending on the polarity score associated with the aspect emotions. Existing neural architectures combining LSTM and CNN employ only the implicit information from the dataset for sentiment classification. Alternatively, this paper highlights the integration of explicit knowledge from the external database (RecogNet) with the implicit information of the LSTM model to improvise the sentiment accuracy. Incorporating sentic and semantic clues from the RecogNet knowledge base to the LSTM increases aspect extraction and categorization efficiency. Furthermore, we implemented CNN with target and position attention mechanisms over the RecogNet-LSTM layer to further enhance the classification accuracy. Finally, the model evaluations are performed using five online datasets related to the restaurants, laptops, and locations. Among LSTM based hybrid models, our RecogNet-LSTM+CNN model with attention mechanism showed superior performance in aspect categorization and opinion classification.

The revolutionary expansion in processing and storing mechanisms through the internet has given rise to affordable and powerful computing features. These qualities have been enhanced as a result of this growth. Computing in the cloud is an emerging technology that provides users with the ability to access data storage facilities as well as application access facilities in an online setting. This approach presents an almost infinite number of chances and difficulties. In light of this, ensuring the safety of one’s data and preventing the accumulation of duplicate or otherwise identical information in the cloud are both highly critical concerns that must be handled. As a result, a method known as deduplication was created to cut down on the amount of data in the identical storage system. In this investigation, a unique approach for removing repetitive or duplicate data from cloud servers, which will also help reduce the amount of internet access and storage space needed, has been presented. The results of the experiments show that the suggested system not only offers a higher level of protection for the data that is stored in stored in the cloud but also addresses the primary issues of rent systems.

Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks.

In recent days, providing data security over cloud is a complicated process. There are many research works that are developed for authentication based data security over cloud, using cryptographic methods. In contrast, physical rules are used for encrypting data. When the quantum models are appeared, it is called quantum cryptography. And, the key distribution in such models is called, Quantum Key Distribution (QKD). Using QKD in securing data is more effective against several attacks. This paper develops a novel simulation model called Secure Quantum Key Distribution for Cloud Data Security (SQKD-CDS) Model. For encrypting the user data, the simulation model uses Non-Abelian Encryption (NAE) for providing secure data security and, further, the quantum key is used for accessing the stored data from cloud. Moreover, the keys are shared between nodes in secure manner using the quantum channel. This proposed simulation model is evaluated using cloud simulator. The results show that the proposed simulation model outperforms other classical security simulation model in terms of efficiency, time complexity and computational complexity.

The reliable and an economic operation of the power system rely on an accurate prediction of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) with hybrid feature selection namely RMR-HFS-LSTM, is proposed. The objective of this study is to reduce the curse of dimensionality, reduce the overfitting and improve the accuracy of short term load forecasting. The RMR-HFS is a combination of filter and wrapper feature selection introduced for identifying optimal subset of features. The instance based RReliefF and infor-mation theoretic based mutual information filter feature selection are utilized to reduce curse of dimensionality by finding and eliminating irrelevant features. The selected features of filter feature selection is tuned by using Recursive Feature Elimination (RFE) wrapper feature selection to reduce overfitting. The deep learning based LSTM improves the accuracy by handling uncertainty issues. The experiment was conducted on European weather and electricity load data using python on Tensorflow environment. The performance of the proposed RMR-HFS-LSTM model is compared against Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The result shows that the proposed RMR-HFS-LSTM model outperforms other models.

The Internet search has become a regular activity for billions of web users to find information. Users typically rely on search engines for information retrieval. Thus, it has become increasingly important for users to find the best results for their queries. There are nevertheless challenges to provide the most efficient, appropriate, and trustworthy results to the user in every web search environment. But there exist numerous unwanted repetitive web pages that escalate time complexity and indexing space issues; hence, identifying and eliminating such pages become a necessity for the communities responsible for information retrieval and web mining. The main aim for web content mining is to identify the duplicate web page content in web search engine. The literature shows the lack of a complete and time efficient duplicate detection model for web search optimization. The present study has identified this need for an enhanced duplicate web page detection technique to improve web search. Increasing the Web site usability and user satisfaction is the most crucial factors in web page detection scenario.

The forecasting of electricity load is an important task for the proper functioning of power system utilities. In the modern era, the dimension of the dataset becomes the barrier for achieving an accurate forecasting results. The dimension of the dataset can be reduced by eliminating the redundant and irrelevant features from the input data. In this paper, the entropy-based feature selection (EBFS) is utilized for identifying both the irrelevant and redundant features. The EBFS removes the irrelevant features using the information theoretic-based mutual information and removes the redundant features using the correlation-based symmetric uncertainty. The random forest (RF) is utilized to forecast the short-term load, and the performance of forecasting is compared against the back propagation neural network (BPNN). The experiment is conducted using R tool on the Australia electricity utility dataset. The result shows that the random forest with selected features achieves more accurate result than others.

Now a day, all the organizations collecting huge volume of data without knowing its usefulness. The fast development of Internet helps the organizations to capture data in many different formats through Internet of Things (IoT), social media and from other disparate sources. The dimension of the dataset increases day by day at an extraordinary rate resulting in large scale dataset with high dimensionality. The present paper reviews the opportunities and challenges of feature selection for processing the high dimensional data with reduced complexity and improved accuracy. In the modern big data world the feature selection has a significance in reducing the dimensionality and overfitting of the learning process. Many feature selection methods have been proposed by researchers for obtaining more relevant features especially from the big datasets that helps to provide accurate learning results without degradation in performance. This paper discusses the importance of feature selection, basic feature selection approaches, centralized and distributed big data processing using Hadoop and Spark, challenges of feature selection and provides the summary of the related research work done by various researchers. As a result, the big data analysis with the feature selection improves the accuracy of the learning.