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Multimedia services are offered to the demanding user by the multimedia cloud. By the fact, the sudden increase of network users has reduced the service receiving response time. Hence, it is impossible to achieve service satisfaction. Therefore, user experience based characteristic is a main role in the multimedia content acquisition, such as server distribution imbalance, huge user visits and limited bandwidth. In order to withstand these problems, a new multimedia cloud content distribution method is proposed based upon the integrated user utility and interest discovery. Initially, the interest features of users are extracted through applying an important feature extraction method. Subsequently, the separation of service and non-service users are formed through the development of a group depending on the categorization of same service interest and adjacent region included users. Followed with this, the integrated utility value is adopted to introduce user evaluation strategies. The integrated utility values are computed combining different user experience characteristics such as, user reputation, user selfish behaviours and user physical performance. However, the service user number evaluated by employing the Opposition Grasshopper Optimizer (OGHA) has minimized the content distribution time and user cost. Furthermore, the convergence profile and computational speed of standard GHA is enhanced by introducing the notion of opposition based population initialization in the proposed approach. Simulation outcomes have evidently proved the improvement of multimedia cloud users, minimizing the total cost of multimedia cloud users, and improvement of multimedia content utilization.

In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection System (IDS) is specifically designed for IoT environments. This system integrates a multi-faceted approach to enhance security against emerging threats. The proposed IDS encompasses three critical subsystems: data pre-processing, feature selection and detection. The data pre-processing subsystem ensures high-quality data by addressing missing values, removing duplicates, applying one-hot encoding, and normalizing features using min-max scaling. A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. The classification subsystem employ two stage classifier namely LightGBM and XGBoost for efficient classification of the network traffic as normal or malicious. The proposed IDS is implemented in MATLAB by using TON-IoT dataset with various performance metrics. The experimental results demonstrate that the proposed SDFC method significantly enhances classifier performance, consistently achieving higher accuracy, precision, recall, and F1 scores compared to other existing methods.

The intrusion detection systems (IDSs) developed based on classification algorithms for securing wireless sensor networks (WSNs) are unable to attain the required detection accuracy. To handle the security issue in WSN, an intelligent IDS is proposed in this work by using a convolution neural network (CNN)-based deep learning approach along with a fuzzy inference model. The proposed IDS keeps track of the network and system activities by using the proposed fuzzy CNN along with spatial and temporal constraints to detect malicious nodes. Moreover, this algorithm has been modelled mathematically by using Feynman Path Integral and Schrodinger equation for handling the spatial and temporal constraints with fuzzy rules. From the experiments conducted in this work, it is proved that the proposed IDS increases the security, detection accuracy and packet delivery ratio, but decreases the delay and false positive rate in WSNs when compared with the existing IDSs.

The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.

Childhood pneumonia is a major contributor to illness and death in children under the age of five globally. Despite advancements in medical science, the burden of pediatric community-acquired pneumonia (CAP) remains high, particularly in low- and middle-income countries. This systematic review aims to synthesize existing literature on the prevalence, risk factors, and healthcare-seeking behaviors associated with pediatric CAP to inform the development of targeted community-based interventions. An extensive search of various databases such as Medline, EMBASE, Web of Science, Cochrane, PubMed, PubMed Central, Helinet, SpringerLink, Google Scholar, and Biomed Central was performed, resulting in 65 potentially relevant studies. After a thorough evaluation process, 25 studies were selected for the final analysis. These selected studies offered valuable information on the epidemiology, risk factors, and healthcare-seeking behaviors associated with childhood pneumonia. The review revealed that environmental factors such as indoor air pollution, overcrowding, and exposure to tobacco smoke are significant risk factors for pediatric pneumonia. Additionally, socioeconomic factors, including poverty and a lack of access to clean water and sanitation, contribute to the vulnerability of children to this disease. Poor healthcare-seeking behaviors, driven by limited knowledge and awareness of pneumonia symptoms and treatment, further exacerbate the situation. The review also highlighted the critical role of vaccination, particularly against type b (Hib) and pneumococcus, in preventing pneumonia. However, gaps in vaccination coverage and challenges in accessing healthcare services remain barriers to effective pneumonia control. In light of these findings, the review recommends the implementation of community-based interventions that address the multifaceted determinants of pediatric pneumonia. These interventions should focus on improving environmental conditions, enhancing access to preventive measures such as vaccination, and promoting better healthcare-seeking behaviors through education and awareness campaigns. It is essential for healthcare providers, policymakers, and community members to collaborate in developing and implementing culturally appropriate and sustainable interventions. This cooperation aims to lessen the impact of pneumonia on children and their families.

Sentiment Analysis of social media tweet data involves the interpretation of people’s opinions, emotions, likings, and tendencies expressed in the form of text. Text acts as an essential form of expression written in a language which finds a wide variety of insights for variety of business applications. The major characteristics of texts on social media are randomness, brevity, uncertainty, ambiguity and complexity which pose problems such as feature extraction and polysemy. In order to solve this problem, hybrid deep learning model with fusion attention mechanism for text-based Sentiment analysis has been proposed. The proposed hybrid deep learning model collates Convolutional Neural Networks (CNN) for drawing out the local information and Bi-directional Gated Recurrent Networks (Bi-GRU) to pull the background connection with the text to optimize the emphasis on textual words that have a strong emotional inclination. Social media tweet data from Facebook, Whatsapp, and Twitter are collected and utilized to carry out the experimentation. The research explores semantic sentiment analysis based on the attention mechanism to analyze classification prediction for evaluating both the favorable and detrimental views of the public. Experimental results revealed that the proposed model showed a significant impact in identifying the sentiments of the tweets effective at extracting features from the text and categorizing them than its counterparts with high accuracy.

Wireless Sensor Network (WSN) has tiny sensor devices that sense the environment, collect the environmental data and transmit it to the sink by using collaborative processing. WSN are applied in variety of applications including healthcare, traffic monitoring system, disaster management system, etc. The resource constraint nodes and unattended environment in WSN make challenges in the energy efficiency. To address this challenge, various researchers have proposed numerous mechanisms to provide energy efficiency during routing process. Among them, cluster oriented routing is the best technique to provide efficient energy optimization to the nodes in WSN. Recently optimization-based clustering and routing techniques provides best solution to address the energy optimization problem in WSN. In this paper, survey on optimization and clustering based routing techniques is presented. Moreover, a comprehensive comparison with various performance metrics has been carried out to understand the efficiency of optimization algorithms for providing efficient routing and clustering process in the network. Finally, this survey work provides the future directions for the upcoming researchers to device various mechanisms on optimization based routing and clustering in WSN.

Attribute-Based Encryption (ABE) is one of the public-key cryptographic techniques which enforces a new method of security through effective control of access on the data which is stored or communicated in encrypted form using a variety of user defined access restrictions. ABE is implemented in a fully-fledged computer system, such as smartphones or embedded devices and it can be also used to enhance the security of network communication. In Attribute-Based Encryption, the encryption key will be associated with a set of attributes and these attributes are useful in the decryption process as well. Encrypting the data for numerous receivers can also be done with ABE, in such a way that only those with the right permissions can decrypt it later. The ABE provides a high level of security due to the wide range of key based qualities that can be defined, and it is also flexible enough to be used in a variety of communication and storage situations. In Wireless Sensor Networks (WSNs), the information is transmitted through wireless links from the sensors to a central location called the sink node. The nodes present in WSN are required to carry out the task of monitoring the environments, gather the necessary data through sensing, encrypt them and send them to the sink node. Therefore, security is a major concern for wireless communication since data is transmitted via an open wireless channel that can be accessed by malicious intruders. In order to handle the security problem, we propose a new Attribute-Based Encryption and Trust Based Secure Routing Algorithm (ABE-TBSRA) strategy by combining bilinear pairing and Diffie Hellman encryption scheme with RSA algorithm to encrypt and secure the data at the sender-side itself. In addition, we propose a new trust-based security model that uses three types of trusts namely direct, indirect, and historical trust values for improving the security. The major advantages of this proposed encryption and trust based secure routing algorithm include the enhancements in security level as well as packet delivery ratio, reliability of transaction and also the network throughput with reduced energy consumption and delay.

Underwater Wireless Sensor Networks (UWSNs) are the type of WSNs that transmit the data through water medium and monitor the oceanic conditions, water contents, under-sea habitations, underwater beings and military objects. Unlike air medium, water channel creates stronger communication barriers. In addition, the malicious data injection and other network attacks create security problems during data communication. Protecting the vulnerable UWSN channel is not an easy task under critical water conditions. Many research works proposed in the literature used cryptography principles and intelligent intrusion detection systems to secure the network activities from malicious nodes. However, the need for Machine Learning (ML) and Deep Learning (DL) associated Medium Access Control (MAC) principles is expected for handling the barriers in uncertain UWSN. In this regard, this article proposes a new Intrusion detection system with Integrated Secure MAC principles and Long Short-Term Memory (LSTM) architectures for organizing real-time neighbor monitoring tasks. The proposed system implements Generative Adversarial Network (GAN) driven UWSN channel assessment models and Secure LSTM-MAC principles to protect the data communication. In this regard, the proposed model creates the Intrusion Detection System (IDS) using trained distributed agents. These agents run in each legitimate sensor node contain novel LSTM-MAC engine, intrusion dataset, rule-based monitoring techniques, Secure Hashing Algorithm-3 (SHA-3), Two Fish algorithm and packet filtering tools. The proposed LSTM and agent-based model drives adaptive MAC channel operations to avoid malicious traffics in to legitimate nodes. In addition, this work implements neighbor-based packet monitoring, signal jamming and alert messaging procedures to build reliable security services against different types of attacks. The experiments and the observations reveal the performance of proposed techniques is proved to be 5% to 10% higher than existing techniques in various aspects measured with different metrics.