The Smart Grid is a major technological advancement that has been made in the energy business. It enables better production, environmental sustainability, and responsiveness to renewable energy sources. The incorporation of Internet of Things (IoT) devices into the Smart Grid, on the other hand, introduces whole new security concerns that must be resolved immediately. The implementation of cryptographic protocols with the intention of bolstering the safety of internet-of-things (IoT)-based smart grid systems is the purpose of this project which can be patented. The research investigates particular safety concerns that are associated with Internet of Things-based smart grids. These concerns include data breaches, unauthorized access, and potential consequences. This work contributes to a secure and resilient IoT-based Smart Grid ecosystem, which provides reliable energy supply in the face of escalating digital threats. This is accomplished by strengthening the security posture of Smart Grids, which is the focus of this study. By examining a compiled table, stakeholders may determine which method will best meet their specific requirements for safety and then make their decision.

In this study, the researchers investigate the mutually beneficial relationship that exists between machine learning (ML) and big data, highlighting both the benefits and the challenges that are brought about by the combination of these two domains. Large datasets provide a difficult environment for machine learning (ML) algorithms to extract meaningful insights from, but ML also provides sophisticated tools to assist in navigating through the complexity of these datasets. Even while there is the potential for synergy, their full-fledged adoption is delayed by significant constraints such as the quality of the data, the need for computing, concerns around privacy, and the skills gap. In addition to highlighting recent advancements in algorithmic efficiency, data preparation, and ethical frameworks, this paper investigates the strategic strategies and techniques that are used to address these issues. An in-depth investigation of machine learning models on a variety of datasets is presented, which offers insights into the efficacy of these models and their applicability for instances that occur in the real world, particularly those that involve the pharmaceutical industry. The research sheds light on the ways in which the environment is undergoing transformations and the ways in which practitioners are actively searching for and putting these challenges into practice in order to fully grasp the revolutionary potential of big data and machine learning.

The distinct parts of a multilayered security network have been designed in such a manner that the susceptibility of a single layer does not impact the additional layers, and therefore the entire network is not susceptible. As this paper provides a multiple-layer protected Internet of Things (IoTs) platform idea depending on machine learning (ML), this research will detail the creation of a multilayer security network system for authentication using ML. Employing a fuzzy inference structure, this work created a multi-layer random forest system for intrusion detection. In this study, the advantages of the filtering and wrapping methods are merged to develop an increasingly sophisticated multi-layer selecting features methodology that improves system security.

The Smart Question Paper Generator using Oracle APEX is a software application designed to streamline the process of generating question papers. It uses Oracle Application Express (APEX) as a development platform to create an intuitive and userfriendly interface that allows educators and examiners to easily create and generate question papers. The system utilizes a database of questions that can be sorted by topic, difficulty level, and type of question. With this information, the system can automatically generate a customized question paper based on the criteria specified by the user. The application saves time and effort, reduces errors, and ensures consistency in the quality of the question paper. Overall, the Smart Question Paper Generator using Oracle APEX is a powerful tool that simplifies the question paper generation process and increases the efficiency of educators and examiners.

A WSN is a wireless network comprising small sensor nodes to monitor environmental or physical parameters. Since wireless sensor networks have limited computational power, memory, throughput, and energy, traditional security solutions designed for resource-rich systems are inappropriate. Given these limitations, providing basic security methods for data transfer in wireless sensor networks is vital. Our work is divided in three phases. Phase 1 focuses on development of a pairwise key management technique. We suggested broadcast tree construction for a wireless sensor network in phase 2. In phase 3, we proposed an enhanced watchdog strategy as a practical way of detecting rogue nodes. The main objective of this model’s depiction is to emphasize how important it is to reduce network power consumption to prolong network lifespan and identify and terminate rogue nodes before they broadcast packets. Experimental analysis shows that our model provides better results than state of art systems.

Mobile edge computing relies heavily on smartphones (MEC). The proper operation of cloud services depends on the security of data stored on mobile devices. Malicious Android applications are becoming more common. To create effective malware detection systems, it is imperative that these apps be thoroughly examined for their malign intent. Machine learning (ML) techniques based on hybrid features may be critical in the detection of Android malware, according to the current most up-to-date model. The feature selection process is critical for capturing the right behavioral patterns of malware instances in order to create a usable categorization of mobile apps. Using both static and dynamic elements of Android applications, we've developed a new method for identifying malware. Malware detection techniques are evaluated on their ability to identify hybrid elements. An F-measure score of 97 percent indicates that the recommended set of features is successful in identifying malware risks. Keywords: Edge computing, smartphone, machine learning, malware detection.

Independent code generation models produced by generative AI provide a new way to software development. These models automatically generate code using machine learning based on input samples. This study examines the fundamentals, applications, problems, and future prospects of AI-related automated code generation technologies. Model-based software, domain-specific code, and testing procedures are examples of these research topics. Performance analysis and assessment are used to assess the efficacy, efficiency, and reliability of several automated code generating methods. The fact that these models have pros and cons and room for development is highlighted.

Using environmentally friendly industrial practises is one way to lower CO2 emissions and the use of natural resources. There has been a lot of work done in the last ten years to shift the economy from linear to circular. This change requires the replacement of existing raw material acquisition processes with remanufacturing techniques and technologies that are incorporated into current industrial operations. Being forced to transition from linear to circular processes makes this change challenging for enterprises. For this shift to happen faster, we must use the technologies of the fourth industrial revolution. To improve the mixed design of the RAC for various compressive strength classes, gradient boosting in regression trees and the particle swarm optimisation technique were both used. With regard to a wide range of objective compressive strength classes, the hybrid method generated RAC combination designs with a diminished environmental effect and significant cost savings. With the highest proportion of recycled aggregate, at the lowest cost, and with the least negative effects on the environment, the process may also be used to produce environmentally friendly concrete.

A new technology called mobile edge computing (MEC) has been recognized as a key component of the 5G network. Researchers propose to use MEC's IT service environment and cloud computing capabilities at the mobile network's edge to assure the implementation of Internet of Cars, Internet of Things (IoT), and self-driving cars. MEC has unique qualities that need a deliberate approach to MEC implementation. Even more so, MEC-enabled service deployment frameworks are very rare. This motivated us to conduct a comprehensive review of existing studies on safe deployment. Many studies and tests have been conducted, but there are few systematic summaries that summarize essential ideas and development approaches for safe commercial implementation of MEC services. An exhaustive evaluation of similar achievements is provided in order to narrow the gap.

The QCA is a revolutionary dominating transistor-less computational nanotechnology based on quantum dots. As such, it might be used in the next generation of quantum computational nano-electronics devices. New molecular materials like QCA are being developed for use in nanoscale devices and cables. The major objective of this research is to optimize the design and investigate numerous properties of a QCA reversible logic circuit design. These locations include, limited Toffoli Gate (TG) and Peres Gate (PG). To address the key problems associated with the physical integration of digital circuits is power consumption and power dissipation which leads to synchronization issues, this research proposes a novel reversible logic gates (RLG-TG) a single layer coplanar approach. RLG designs with minimal design area, latency, and quantum cell count (QCA) are given and implemented using a Bijection functional method. Using the QD-E (Energy) tool, the first-order energy dissipation of the proposed shape and the impact of output bias cell temperature are also investigated. The proposed circuit designs were tested using the CVSE parameters, which had high clock signal saturation energies of 9.8e-22 d (Jules), recovery times for damping factors of 1e-15 s, and relative dielectric constants of 12.90 for GaAs and AlGaAs. The number of quantum cells used by the described new RLG-TG and RLG-PG designs is decreased by 38.23 % and 21.14 %, respectively, when compared to the optimal RLG designs employed in the state-of-the-art RLG designs. In this investigation of the proposed four-bit EPG and OPG circuits occupies 18.91 % and 38.27 % less design area, requires 46.15 % and 46.25 % less number of cells, and both designs has been 66.66 % improvement in delay.

The prevalence of social media sites like Twitter has made it simpler for individuals and organizations to disseminate incorrect facts or misinformation that can sway public opinion and behavior. It is crucial to create a trustworthy system that can recognize the sentiment of tweets in their context, analyze that sentiment, and pinpoint those that are spreading quickly and could be potentially damaging or deceptive. Hence, to identify viral tweets on Twitter, we suggest a new remora-optimized twofold gated attention neural network (RO-TGANN). This research’s word representation also creates weighted word vectors by including sentiment data in the term frequency-inverse document frequency (TF-IDF) algorithm. The resulting vectors are entered into RO-TGANN to better represent the comment vectors and efficiently collect context information. Multi-layer perceptron (MLP) classification is also employed to determine the sentiment pattern of the message. The proposed technique is contrasted with the current sentiment analytical techniques under comparable circumstances. According to the empirical results, the suggested analytical approach for sentiment classification has a greater accuracy, f-score, precision, and recall. The creation of such a technique can aid in the drive to encourage ethical social networking usage and limit the transmission of dangerous posts on social networking sites.

The usage of mobile cloud computing (MCC), which allows mobile users to access the benefits of cloud computing in a manner that is favourable to the environment, is an efficient strategy for addressing the present demands of the industrial sector. MCC is one of the acronyms for "mobile computing in the cloud." As a result of limitations imposed by wireless bandwidth and device capability, the installation of MCC has been met with a range of obstacles. These difficulties consist of, among other things, an increase in the amount of energy that is wasted and a delay in latency. To solve this issue, we have presented a dynamic cloudlet-based mobile cloud computing model (DECM), which makes use of dynamic cloudlets (DCL) to manage the additional energy that is necessary for wireless connections. As part of this investigation, we test our model by simulating an event that may take place in the actual world, and we also give data that can be relied upon for the evaluations. This research contributes significantly in two distinct ways. To begin, this study is the very first analysis of the most effective strategies for resolving concerns about energy loss within the framework of dynamic networking. It was carried out by a group of researchers from the United States and Canada. Second, the proposed model provides a path and theoretical foundations for more research to be conducted in the future.

This paper presents a novel hand-gesture-based control interface designed to navigate a semi-autonomous vehicle. The interface is wirelessly connected to the vehicle using radio waves. In general, robots are programmed to perform tasks that may be difficult or impossible for humans to accomplish, or that may not be safe for humans under certain conditions. In this paper, we describe the setup and working process of our interface, including how it enables users to control the vehicle using dynamic signal intuitive hand gestures. The potential benefits of our interface include improved safety, increased ease of use, and greater accessibility for people with mobility impairments.

In this research, we investigate the uplink (UL) channel of a cellular network that is composed of Internet of Things (IoT) devices by using a reconfigurable intelligent surface (RIS) that has a limited number of different configurations for reflecting angles. The effect of the RIS on the angles at which the signal is reflected is the primary focus of our attention here. Before we can begin to work towards the signal-to-noise ratio (SNR) that we have set for ourselves, we need to first acquire an accurate estimate of the amount of transmit power that is necessary for machine-type devices (MTDs). This equation has to take into account the discretization of the RIS into In order to achieve the highest possible level of accuracy, the components that are being reflected need to be subwavelength. In order to accomplish this goal, it is necessary for you to construct a model of the channel. According to the calculations, the transmit power need to be selected in accordance with the goal signal-to-noise ratio (SNR), the placement of the MTD within the service area, and the design of the RIS, which ought to take into consideration the number and variety of possible reflecting components. This is because the MTDs are the ones in charge of determining the transmit power and the EBL for their respective channels. The numerical simulations on the energy efficiency (EE) examined by the EBL demonstrate the benefits of installing RISs to enable energy-efficient IoT networks. The primary focus of these simulations, which are designed to demonstrate the benefits of deploying RISs in order to allow energy-efficient IoT networks, is on highlighting the primary advantages of doing so. This is done so that the aforementioned simulations can demonstrate these benefits.

This project presents a systematic framework employing advanced deep learning techniques for predicting knee osteoarthritis. Leveraging state-of-the-art models in image recognition, joint segmentation, and pathological analysis, our approach aims to streamline the diagnosis process. The integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) enhances the accuracy and efficiency of predicting knee osteoarthritis. This study represents a significant advancement in medical diagnostics, offering a valuable tool for healthcare professionals to predict and manage knee osteoarthritis, contributing to improved patient care and management.

Clustering is an efficient approach for boosting network durability, energy efficiency, and sensor node connectivity in a wireless sensor network (WSN). The WSN routing protocol has been thoroughly investigated. Based on the network organization, the wireless sensor network has been classified into three types: flat routing, location-based routing, and hierarchical cluster-based routing. Cluster-based routing, because of certain advantages is more efficient in routing technology. This research paper presents the outcome of a large scale survey that was done on cluster-based routing algorithms in WSN. Finally, the authors discuss the highlights and challenges of clustered routing techniques.

This study introduces a novel Internet of Things (IoT) assistive technology designed to increase the mobility and self-sufficiency of visually impaired people. Over 40 million individuals, including over 1.6 million children, are visually impaired in India, and getting around in daily life is still exceedingly difficult. Walking canes and other traditional aids are not always effective in identifying hazards at different heights and angles. The suggested system integrates smart shoes and spectacles with buzzers, microcontrollers, and ultrasonic sensors. The smart glasses recognise obstructions at head height by extending detection beyond the level of the shoes, which continuously monitors the ground for obstacles. The system ensures better spatial awareness by alerting users in real-time with audio when an obstacle is spotted. The IoT-enabled communication between shoes and smart glasses is a significant innovation since it enables synchronized obstacle identification and reaction. With its all-encompassing coverage, this integrated strategy enhances user mobility and safety. The system is lightweight, ergonomically sound, dependable, and made for everyday use. Future additional improvements might include GPS integration for accurate navigation and machine learning for anticipatory obstacle identification. This study demonstrates how people with visual impairments can live much better thanks to wearable Internet of Things devices.

Deep Neural Networks (DNNs) are going to be used in this study with the intention of achieving the objective of optimising the resource allocation and interference control of vehicle radar systems. Because contemporary automobiles depend heavily on radar technology for both their safety and their capacity to engage in autonomous driving, reliability is an essential characteristic of vehicular radar systems. Despite this, there is an increasing amount of congestion in the electromagnetic spectrum, which makes it more difficult for radar systems to perform their functions. Utilising DNNs as a tool for resource allocation is a method that shifts the paradigm by optimising accuracy, efficiency, and flexibility to the fullest extent possible. For the purpose of this study, a comprehensive review of the existing literature on a variety of topics, including smart grids, the energy harvesting potential of wireless networks, and the use of DNNs in automobile radar systems, is carried out. The article presents a comprehensive review of smart grids and coordinated multi-point communication, with the primary emphasis being placed on collaborative energy use and communication. This article investigates the fundamental ideas behind DNNs, as well as their applications, advantages, and challenges when applied to automobile radar systems.

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (