The practice of steganography involves the concealment of confidential data inside other, seemingly innocuous files of the same or other sorts. The objective of this study is to create a stego technique that, when applied to a video clip, will successfully conceal a message inside its graphics. A model is developed for video steganography by developing a model to conceal video inside another video using hybrid convolutional neural networks (HyCNN). The second objective is to expand the size of the space that can be used for hiding, which has been accomplished via the use of CNN. The suggested model was trained using HyCNN on arbitrary pictures drawn from the ImageNet database. The findings also show that the system is able to produce excellent results in visibility and attacks, where the suggested approach is able to effectively mislead both the observer and the steganalysis software.

An important part of intelligent transportation systems (ITS) is the use of car ad hoc networks, or vehicular ad hoc networks (VANET). There are still a lot of security issues with VANETs, including catastrophic blackhole threats, even though they have a lot of benefits. The deep-learning-based secure routing (DLSR) protocol and the deep-learning-based clustering (DLC) protocol are the part of this work. The DLSR protocol uses deep learning (DL) at each node to decide between secure routing and normal routing. It also builds safe routes at the same time. It's also possible to find out what bad nodes are doing, which helps us choose the best next hop based on how well its fitness function works. To make the fitness function better in both the protocols, we build a deep neural network (DNN) model. The proposed system improves the localisation accuracy.

Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services.

In recent years, online education has been given more and more attention with the widespread use of the internet. The teaching procedure divides space and makes time for online learning; though teachers cannot control the learners accurately, the state of education calculates learners’ learning situation. This paper explains that the discourse analysis method is utilized to examine the online teaching behavior of teachers and student behavior like class attendance, how many are active in class, and learning behavior in online education. Also, discourse analysis will optimize and enhance the classification of the text understood according to their language. After pre-processing, feature extraction was done by utilizing Term Frequency-Inverse Document Frequency, and feature selection was calculated via utilizing chi-square examination for teacher discourse like learning behavior, languages understood by students, and language types. Moreover, the machine learning-based classification technique Support Vector Machine (SVM) is considered to analyze the teacher discourse in class automatically, and results are compared with existing techniques.

In this day and age, there has been a discernible focus on and significant usage of Quantum machine learning (QML) models with the intention of predicting the toxicity of inconsequential compounds. The application of computational toxicity prediction provides considerable advantages in the early phases of pharmaceutical research. These benefits include the identification and removal of compounds that are likely to display poor effectiveness when tested in clinical trials. This trend has been easier to observe with the introduction of extensive toxicity databases. As a result of the fact that this field is still in its infancy, it is essential to acquire a more all-encompassing grasp of the range of QAI approaches and the contexts in which they might be used. Trials to harmonize principles from quantum mechanics, Quantum Machine learning algorithms with classical ML techniques, which leads towards enhancing the interpretability has been performed. In an attempt to achieve robust accuracy associated with the QML model, reach out of enriching insights from naïve Bayesian classification and recursive partitioning, through the merge of sophisticated computational techniques with complex biological phenomenon has been done, the present study not only moves towards enhancing the repertoire of tools available for early stage enhanced drug discovery optimization but also moves towards revolutionary possibilities of quantum-infused methods in tackling persistent issues in the field of bioinformatics and toxicology.

The Q-learning approach, within deep reinforcement learning (DRL) methodology, is beneficial in multifarious tasks, one of them including enhancement of navigational tasks, shown by the DQN approach; merging Q-learning with deep neural networks is not without its challenges, particularly in situations where there is a chance of overestimating the correct value. In order to address the problems described above in the realm of electric and hybrid vehicles (EHVs), a quantum-computational adaptation that is influenced by Double Q-learning as a potential solution is proposed i.e., QED2Q-N. The enhanced version increases the efficacy of navigation by addressing the overestimates, which were included in previous editions, conceived with the intention of approximately modeling functions on a broad scale. The expansion of electric and hybrid vehicles (EHVs) toward growing autonomy, along with improvements in intelligent transportation systems (ITS), pose significant cybersecurity threats in the disciplines of vehicle automation and cooperative ITS. These underlying dangers may be found in the sectors of vehicle automation and cooperative ITS with an emphasis on the ways in which quantum-enhanced systems may assist in strengthening defenses, the use of quantum computing and deep reinforcement learning (DRL) merged together, works to bring improvement in EHV navigation accuracy and the enhancement of overall system security. The study underlines the relevance of adopting a quantum-computing technique in order to properly solve the dynamic issues associated with EHV technology.

The advent of 5G technology has transformed the way that wireless networks and Internet of Things devices communicate, offering new opportunities in healthcare applications. Orthogonal frequency division multiplexing (OFDM) offers high data rates, low latency and robust communication in difficult settings, is one of the main 5G technologies. Healthcare applications face many difficulties despite 5G and OFDM's potential. The previously mentioned factors include reduced latency, dependability, energy conservation, data protection and privacy. To overcome this, we proposed wavelet-enhanced multiple input multiple outputs-orthogonal frequency division multiplexing (MIMO-OFDM). This study uses a variety of modulation schemes, including binary phase shift keying, quadrature phase shift keying, 8-PSK, quadrature amplitude modulation (QAM), 8-QAM and 16-QAM across additive white Gaussian noise channels. To simulate the proposed method by using MATLAB. The proposed systems in healthcare applications with limited resources may be restricted by the complexity of their implementation. To improve data transmission performance for healthcare applications, its viability depends on resolving complex difficulties and customizing it to particular medical applications.

The identification of diseases in plants contributes an important role in captivating disease control methods for the improvement of quality and quantity of crop yield. Mango trees are affected by different diseases and the identification of diseases is a tedious task till now when those diseases are manually detected. This paper proposes the novel hybrid Coyote Grey Wolf optimization (CO-GWO) algorithm for the classification of mango leaves as normal or diseased. The classification process is done through the extraction of significant features from the segmented image. The Neural network (NN) classifier performs the classification task, with the weights being adjusted using the proposed algorithm that acts a major role in the enhancement of the classification accuracy. The effectiveness of the proposed model is evaluated concerning the evaluation metrics, namely accuracy, precision, recall, and F1 measure, and is attained to be 96.7111%, 97.5712%, 97.1504%, and 96.4792%, respectively. This shows the superiority of the proposed technique in the effective classification of mango leaf classification as compared with the existing techniques.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Devesh Sharma, Ravi Kumar, Rajesh Kumar Vishwakarma; A compact dual-band modified rectangular-shaped MIMO antenna for wireless applications. AIP Conf. Proc. 5 January 2024; 3000 (1): 020009. https://doi.org/10.1063/5.0189019 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

The coronavirus outbreak is a recent pandemic that destroyed most of the lives, economy, and livelihoods. The detection of COVID-19 is the main aim to detect and provide better treatment for patients to mitigate its impact. In addition, it is necessary to diagnose the disease swiftly with upgraded technologies. This can be achieved by CT image scanning. This provides the fastest detection of the disease. Moreover, it can also be used to diagnose the percentage of the affected lung areas. To perform this fastly, we propose a novel approach known as Convolutional Neural Network (CNN)–based Improved Grey Wolf Optimization (IGWO) algorithm. The proposed CNN utilizes a SegNet-based approach which can be used to detect the affected area in the lungs by using the encoder and decoder steps. The encoder in this approach uses three types of CNN architecture. First, the decoder is used to reconstruct the images. The overfitting issues during the iterations and complexities are reduced by adopting the IGWO approach. The experimental analysis depicts that the proposed approach effectively segments the CT images and promptly diagnoses the affected lung area.

Cloud computing has been adopted by a wide variety of businesses and organisations to give services to customers in a secure and certified manner, protecting cloud providers from fraudulent actions. To investigate cloud-based cybercrimes, however, cost-effective forensics and successful implementation is essential. The topic has been the subject of several surveys and reviews thus far from researchers. An iCloud investigative tool taxonomy is presented in this study to find the products that meet their technical needs in a searchable catalogue. The authors of this study developed the taxonomy. The research results demonstrated that the recommended solution may effectively help digital inspectors in their mission to look into cloud-based cybercrimes. This research paper aims to analyse the digital forensics issues raised by the cloud computing paradigm and to offer the appropriate solutions and recommendations. Cloud computing and more conventional types of digital forensics are also given in-depth examination.

The management of crops from the early to mature stage contains nutrient deficiency, monitoring plant disease, controlling irrigation, and controlling the use of pesticides and fertilizers. Moreover, lack of immunity and climate changes cause the crops and minimize the growth of agriculture due to crop disease. The identification and detection of crop diseases is the most challenging task due to less detection accuracy, overfitting, and error rate. So this research work designed a novel Krill Herd based Random Forest (KHbRF) for the accurate detection of crop disease, enhancing the performance of detection accuracy by using an optimized fitness function. The krill herd fitness function is updated to the classification layer for effective crop disease detection. Furthermore, development involves preprocessing, segmentation, feature extraction, and classification. The developed framework is implemented in the python tool, and the plant villa image dataset is tested and trained in the system. After that preprocessing removes errors and feature extraction extracts the texture features from the crop. At last, the classification layer detects the crop disease present in the dataset using the fitness of the krill herd. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure, and error.

The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing $A I$ techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms.

Rural residents are gradually beginning to agree with their urban counterparts. Site-specific tweaking of administration requires precision farming since it accounts for soil nutrients that are specific to the needs of each crop. Careful planning is necessary to optimize yields, but a precise evaluation of the soil's capabilities and constraints is also crucial, as it will form the basis for choosing the right manure, application quantity, and timing. Farmers' Preparation times are notoriously hard to estimate, so you'll need to depend on gut feelings, trial and error, a healthy dose of mystery, and critical thinking. Inefficient outcomes, wasted resources, and exacerbated environmental harm are only some of the numerous negative consequences of these issues. Because of a lack of information, farmers often have no idea how their decisions will influence their crop yields or the state of the environment. Based on the results of this research, it seems that adapting manure management strategies to the specific needs of certain crops and regions might help mitigate the negative effects of excess fertilizer and manure on the environment. By using artificial intelligence and big data analytics, the agri-food industry has the potential to make significant contributions toward meeting the growing food demand throughout the world and attaining sustainability in spite of the many challenges it faces. Soil samples might be sent to universities for analysis; however, this method is ill-considered, time-consuming, and unreliable. Recommendations such as predicted compost, NPK supplementation, and application time may be generated using weather prediction and an ANN's development.

Develop a novel Krill Herd-based Convolutional Neural (KHbCN) scheme to identify and diagnose crop diseases accurately. Using an improved krill herd fitness function, the proposed model can identify crop disease reliably and improve detection performance. The krill herd fitness is updated to the convolutional neural network (CNN) to diagnose crop damage accurately. The created framework is executed in Python, and the system is evaluated and trained using the plant villa dataset. After removing mistakes during preprocessing, feature extraction is used to extract texture features from the crop. Finally, the constructed model uses the fitness of the krill herd to identify crop illness. By recognizing and detecting agricultural diseases, the primary goal of building a convolution-based optimization model is to enhance the growth of agriculture. The experimental findings of the framework's development are contrasted with those of other cutting-edge methods that achieve 99.85

Phishing, a prevalent cyber danger in contemporary times, involves the fraudulent impersonation of legitimate websites with the intention of deceiving users into divulging confidential information. The insufficiency of classic phishing detection tools has become apparent as fraudsters continue to develop new strategies; these approaches mostly depend on variables such as URL character sequences, site content, and visual resemblance. The present study highlights the “Zérosdetect” approach, a quantum-powered technique for detecting phishing URLs using zero-shot learning, a robust model that makes use of both the zero-shot learning’s adaptability and the computational advantageous nature of quantum computing, it mitigates the need for extensive previous knowledge of phishing URL attributes. In order to detect previously undiscovered phishing attacks, quantum neural network have been deployed to convert URL data into quantum spaces, therefore using the computational benefits of quantum systems. Quantum layers embedded inside Qnodes are a key part of the present system, calculating gradients at a faster pace, optimizing the network performance by rapidly calculating gradients, making the solution both efficient and forward-thinking. The present study lays the groundwork for future cybersecurity initiatives in the era of quantum computing, enhancing the potential to predict new cases of phishing.

With the wide popularization of Internet of Things (IoT) technology, the design and implementation of intelligent speech equipment have attracted more and more researchers’ attention. Speech recognition is one of the core technologies to control intelligent mechanical equipment. An industrial IoT sensor-based broadcast speech recognition and control system is presented to address the issue of integrating a broadcast speech recognition and control system with an IoT sensor for smart cities. In this work, a design approach for creating an intelligent voice control system for the Robot operating system (ROS) is provided. The speech recognition control program for the ROS is created using the Baidu intelligent voice software development kit, and the experiment is run on a particular robot platform. ROS makes use of communication modules to implement network connections between various system modules, mostly via topic-based asynchronous data transmission. A point-to-point network structure serves as the communication channel for the many operations that make up the ROS. The hardware component is mostly made up of the main controller’s motor driving module, a power module, a WiFi module, a Bluetooth module, a laser ranging module, etc. According to the experimental findings, the control system can identify the gathered sound signals, translate them into control instructions, and then direct the robot platform to carry out the necessary actions in accordance with the control instructions. Over 95% of speech is recognized. The control system has a high recognition rate and is simple to use, which is what most industrial controls require. It has significant implications for the advancement of control technology and may significantly increase production and life efficiency.

This study includes blockchain technology that handles each mobile database like one block. First, every block detects its data range. The system then links sensor information for every block to blockchain technology. Every block node stores the sensor data for the entire wireless network after the connection of each block is completed. The module also simultaneously supplies a web server. The internet of things (IoT) topology is used to build up this mobile web server. A blockchain is a concatenated transaction record that is cryptographically protected. To preserve the integrity of wireless sensor networks, they need protection against multiple security infractions. Each block of the suggested technique includes the encrypted risk value of the previous block, the current timestamp, and wireless network sensing data. The proposed system, therefore, collects and analyses sensor data to optimise the setup of the wireless sensing network.

In recent years, network-enabled physical devices have emerged as a solution to many problems, such as those involved in transmitting data from the physical world, communicating, and safeguarding information. The process of data transmission is vulnerable to a range of threats and must be secured because an open communication channel is considered for data transmission. One way to eliminate this security risk is by using a reliable and secure mutual authentication method. To accomplish this, a secure and reliable computation-based multifactor authentication approach is proposed for Intelligent IoT-enabled WSNs. To reduce the computational and communication overhead, a physically unclonable function-based secure authentication approach is proposed for sensor node's mutual authentication. In addition, to maintain the confidentiality of session key, a secure session is established among user, basestation, and nodes by using an adaptive session key update procedure. Moreover, formal and informal security analyses are performed for different aspects of security attacks.