Smart cities that use technology and data for efficiency optimization, sustainability, and well-being of citizens face a lot of challenges. Because all of the aforementioned challenges share a common characteristic of complexity, achieving success will need careful preparation and coordinated effort. This research presents a novel approach utilizing deep learning models to address issues about road congestion, specifically by offering secure routes for pedestrians and cyclists. The Global Positioning System (GPS) data stored in the cloud is used as input for the proposed work. In the proposed work, the flow of vehicles, their speed, and the occupancy have been predicted. The need for deep learning to resolve the traffic problem is that deep learning methods are highly efficient when compared to statistical techniques as they provide more than 90

Because of the increasing value of teaching, learning, and schooling, the data-driven university approach has been highly promising in recent years. As each student grows after school via people of all ages, the expanding information science approach continues to be dynamic, as well as excellent learning. Until information is generated inside an enterprise or via the educational system, the learning business has been competitive in terms of volume, diversity, and speed of knowledge. Despite the fact that Big Data is organized, big, and therefore quick, it has lately demonstrated that it is highly fast in terms of functionality when compared to typical data warehouses. He is becoming more knowledgeable about technology. Educators and administrators have depended on an out-of-date Student Information System (SIS), Learning Management System (LMS), Enrollment Management System (EMS), and Admissions System for the past decade to improve and streamline classroom, college, and classroom procedures. To increase teaching and learning, the company has recently incorporated mobile devices, online assessments, e-learning programmers, learning software, and digital tandem boards. For institutions, data is the reality, assisting instructors in understanding why, based on better dissemination of outcomes via university students. Is it impossible to become a professor? Why would a student drop out and flunk school? What causes students to struggle with new ideas or skills? We provide readers with the disciplines of significant academic facts, as well as the extent to which academic data must be scrutinized, while remaining focused on the perspectives of only a few stakeholders, thereby promoting activities based on data pertaining to the characteristics of significant academic facts. By integrating knowledge of data-type data technology, dashboards, and accountability, large data analysts may enhance education and disclose relevant research, evaluation, and accountability. A variety of variables impact the use of the internet and data mining in education: I was thinking about how to increase learning and the implications for individualized teaching approaches like learning, education, or strategies. (ii) Building communities around possibilities for the most up-to-date types of training, In the present system, test and integrate evidence-based decision-making. iii) Enhance privacy, security, and customization, as well as current analytical methodologies, human science skills development, and data use and usage iv) the outcome of final practical applications and analytical concept comprehension After numerous instances of ideas have been provided, associations are employed in health professions research. (v) the availability of real-time high-speed e-mail technology, which necessitates quick access for new entrants and organizations to collect and handle streaming data, resulting in the recording of fraudulent access investigators between sophisticated research institutes and educational institutions. Following these procedures, participants feel that there are a variety of data science approaches that may significantly improve the level of education for individuals of all ages, including academic improving education learning.

Our idea is to find a lost person with the help of efficient manpower by using thematic mapping-creating the heat map will help the search parties and also based on historical analytics, it produces search results which will help us with the idea to search and build some intelligent data for future forecasting. Along with the heat map, the application will also provide possible search patterns and radius. It uses open source data like google maps for the particular radius from missing time and it also uses CNN models. We can also connect GPS gadgets to this application. It is used to find a lost person who is mentally unstable, dementias, old age, children kidnapping, human trafficking. In this application relatives, friends, NGO’s, Police, Area Volunteers help in searching the lost person.

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

In order to accommodate the expected billions of devices, increased data rate, and ubiquitous connectivity of the future 5G mobile networks, a wide range of technologies will need to be implemented. Especially since 5G networks will be tied to core and enabling technologies, sensitive data in the next generation of wireless systems will be sent across all levels. As several recent cases have shown, the threat posed by an infected network of wireless devices can have far-reaching consequences for privacy and security of a whole ecosystem's worth of communications. For this reason, detecting or preventing sabotage has become a global concern as security threats have grown in complexity and intensity in recent years. With an eye on both security and privacy, this paper delves deeply into topics like network system security, physical-layer security, and 5G privacy issues; these are the technologies at the heart of the 5G security paradigm. The article also covers topics related to 5G network security administration and monitoring. This article also gives a brief review of 5G standardization safety forces and assesses the security measures as well as standards of core 5G technology using a variety of standardization approaches. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability.

Wireless Sensor Network attains the major problem of localization troubles and the accuracy of the localization relies upon the localization of sensor nodes with the very best safety necessities for the source community consists of two components which are named as common nodes as well as beacon nodes. The localization system has rather relied upon the common nodes. The proposed method consists of an infrastructure-centric technique. Having an ideal data storage site in wireless sensor networks is the major problem here. The previous study did not employ swarm-intelligence-based optimization methodologies to locate the ideal site for data storage. Thus, the optimization approach for hybrid particle swarm has been applied to locate the appropriate storage node site while reducing the overall energy cost of transmission. The proposed methodology will eliminate the security problem within the common nodes during the localization of sensor nodes of the protocol. The network consists of various random nodes that have the deployment as well as the mobility concern. The implemented protocol consists of Infrastructure-centric localization systems to secure the source node location. Further, our projected set of rules presents low overhead thanks to the employment of abundant less manipulate messages in an exceedingly restricted transmission selection. Similarly, we’ve got to boot projected a formula to return across the malicious anchor nodes within the network. The simulated effects show that our projected formula is inexperienced in phrases of your time consumption, localization accuracy, and localization magnitude relation inside the presence of malicious nodes. Time Difference of Arrival (TDOA) is the major factor indicating the location of nodes at the required position. The performance of the metrics can be analyzed using the energy calculation, alive and dead nodes during the transmission.

Over the years, advancements in the work have been incorporated towards the patient’s relationship and data collection techniques to model a wide variety of human activities and behaviors. Sensor data comes from smart devices that provide the ability to manipulate data from monitoring data for patient healthcare. Due to the high popularity and use of smart devices as respondents, performance recognition systems are more accurate and easier to use. The knowledge model is based on two new approaches: to consider a functional degree scheme between measuring sensor energy and functional consciousness to model controversial sensor data and establish the relationship between them. In this article, we make a case why ontology can contribute to blockchain design. To support this issue, this research proposes an Ontology middleware that analysis and exploits ontology and some of its representations into the smart contracts that enable it to implement patient traceability restrictions on the original traceability feature platform.

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77

Instantaneous data processing has the potential to enhance scalability, lessen power usage, and permit and improve data presentation in Consumer Internet of Things (CIoT) devices. In simple terms, cloud-based solutions cannot handle many IoT applications. According to Industrialized IoT (IIoT) technologies, an automated resource allocation system can improve service delivery and minimize healthcare costs. To maximize resource usage and response time for end users, there needs to be an effective method to efficiently distribute workload between Fog Layer and Cloud Connection and enhance cloud network capital allocation. Data analytics of complex and vital healthcare data requires timely responses, making it complicated. This paper proposes a design based on the Lanner Swarm Optimization (LSO) algorithm, which was developed to overcome inefficient heuristic strategies where data is transported to the cloud layer based on traffic type. The LSO algorithm is used to improve resource allocation and workload distribution in cloud-assisted CIoT applications for smart healthcare systems, improving scalability, power consumption, and data processing. The objective function determines if diverse virtual machines (VMs) vary accomplishment time the most, considering this study's updating and pruning restrictions. The experimentation analysis demonstrated that the proposed load balancing and work scheduling method outperforms evolutionary and heuristics algorithms. In experimentation, the research model attains a makespan of 10 s, response time of 5.5 s, resource utilization with a rate of 0.9, execution time of 13 s, latency of 10 ms, throughput of 0.78 s, and delivery rate of 0.74%. At resource scheduling, the LSO model had the best payload routing, latency, packet delivery ratio, and network lifetime.

Artificial Intelligence has been shaped significantly in this current world. Artificial intelligence builds a path for communicable applications and an easier understanding for the system and the user. By adopting the advantages of artificial intelligence, our project focuses on estimating and delivering the right resources for a college student. The Web App; Edu. Social is created to determine and seek deep skills of a student and provide them with the right resources to heighten their knowledge. The web app would analyze the right resources based on the initial and prime inputs given by the students. The resources comprise placement opportunities, certification suggestions, workshops, seminars, activities and many more that are happening around the respective student. The web app does not just focus on the academic perspective but also the inner passion of a student. Furthermore, a social chat will be enabled in the app itself for student-student communication, student-professor communication to form teams, request for mentors or just ask for doubts. Therefore, this platform would be a great benefit for a student to gain experience and to see the viable resources put forth according to their field of interest and choice.

Internet of Things (IoT) is rapidly developing technologies and the Internet of Things (IoT) is a network of physical items or effects embedded with computer software, detectors, and network connectivity. The Internet of Things also addresses the issue of centralizing physical bias management online. We are leveraging the Internet of Things (IoT) idea to construct a system that will automatically monitor all artificial processes and issue warnings or expert comments when necessary. nowadays, gas leakage is a serious concern in the house and as well as vigilance. If the gas sense is indeed low, it's because we've been too careless, intolerant, or both to look for it. If the gas position is raised, however, calamity may ensue. In this research, the crucial concept is introduced to prevent this catastrophe from occurring. In this study, we propose an alternate approach to this impending calamity and design a system with integrated sensors, controllers, and some IoT-assisted software. The suggested method, known as the Internet of Things based Industrial Safety System (IoTISS), is compared against the more established Industrial Safety System (ISS) to see how well it performs. In this system, we are monitoring the detection of gas leakages with various warning characteristics as well as other devices are used for monitoring various aspects like humidity and temperature using DHT22 sensor, MQ2 gas monitoring sensor, fire sensor and NodeMCU ESP8266 WiFi module. NodeMCU ESP8266, which doubles as a WiFi module, will receive data from all of these types of sensors.

Multi-User Authentication Framework (MUAF) is the process of continuously authenticating the user even after their successful login. It is also known as Active Authentication and it has become a necessity today. Researchers proposed a few MUAF techniques. One aspect of this paper proposes a novel method for MUAF using the health patterns of the user. MUAF methods and solutions exist in the literature are specific to some devices and specific to some applications. There is no generic framework that exists for MUAF. Another aspect of this paper proposes a generic MUAF framework that employs appropriate available MUAF techniques based on the current device, current application, and current user. The proposed framework is generic and it can be used with any device, any application. When we use multiple MUAF techniques, each technique does authenti-cation on its own and makes its independent decision. All those independent decisions will be used to make a global authentication decision. Researchers proposed a few such fusing methods. Another aspect of this paper proposes a novel fusing method using the “Random Forest” concept. Proposed fusing method learns from the feedback provided by the reliable sources and refines itself to make efficient authentication in future.

Chronic kidney disease (CKD) is an international fitness hassle with excessive mortality rate, and it induces different diseases. Since there aren’t any consequences in the beginning stages of the disease, sufferers forget about it. Due to their short and particular acknowledgment execution, machine studying fashions can assist clinicians accomplish this goal. In this assessment, we suggest a Logistic regression device for identifying CKD. The statistics set became from the UCI store, which has a wide variety of lacking characteristics. Due to the fact that sufferers may leave out some estimation for one-of-a-kind reasons, missing features are usually found. After accurately rounding out the fragmented informational index, six AI calculations have been applied to installation fashions. The nice execution was carried out by abnormal forest. By breaking down the misjudgments produced with the aid of using the installation fashions; we proposed an included version that consolidates calculated relapse and abnormal woods with the aid of using perceptron. Consequently, we theorized that this philosophy will be suitable to greater confounded medical records for illness finding.

Abstract In the field of Image Mining (IM) and Content-Based Image Retrieval (CBIR), the significance lies in extracting meaningful information from visual data. By focusing on the intrinsic meaning within images, semantic features enhance the accuracy and relevance of image retrieval systems, bridging the gap between human understanding and computational analysis in visual data exploration. This research explores the fusion of image processing techniques and CBIR. The need for this research is based on the persistent challenges in existing CBIR systems, where traditional methods often fall short of comprehensively capturing the intricate semantics of images. The primary objective of this research is to propose a novel approach to CBIR by implementing the Tokens-to-Token Vision Transformer (T2T-ViT) to address the limitations of traditional CBIR systems and enhance the accuracy and relevance of image retrieval. The T2T-ViT model achieves exceptional performance in CBIR on Corel datasets, with a high accuracy of 99.42%, precision of 98.66%, recall of 98.89%, and F-measure of 99.35%. The model demonstrates a harmonious balance between identifying and retrieving relevant images compared to existing models.

The optimization of data structures and trade-offs is a complex problem. This is simplified and provides solutions through the aid of artificial intelligence. There are various synchronization problems occur due to the concurrent access to the data structures. This affects the overall performance of the system. To overcome various challenges, deep learning with optimization algorithms is implemented. This helps to achieve the potential areas for optimization in multithread software by evaluating the historical patterns and performance information. Trade-offs are defined as the choices that are initiated in the optimization process for data structures in concurrency. Deep learning helps to analyze the impacts of these trade-offs by developing training models on the historical data. A large amount of data is retrieved through the deep learning techniques. This includes extracting image data for CNN and time series data for RNN and LSTM. The reduction of using data compression techniques helps in obtaining optimal performance parameters. The various forms of threats are neglected through data loading and preprocessing stages. The concurrency is essential due to the usage of multiple threats working on the neural networks. This is further accessed through synchronizing access to shared data structures. They must be properly done because excess synchronization leads to the origin of bottlenecks. A balance between data structure optimization and concurrency control must be maintained properly. Excessive concurrency control may reduce the performance. The execution speed of the CNN is enhanced through the aid of hardware accelerators. Thus the fine-tuning of trade-offs and creation of software are done through training and testing to obtain increased use of multi-core processors.

The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.

The tools used in agriculture are evolving quickly. The advancement of farming technology, farm infrastructure, and manufacturing facilities is ongoing. Numerous photovoltaic (PV) systems may be used in agriculture. These use cases include both standalone installations and larger systems put in place by utilities when they determine that PV technology is the most cost-effective way to meet a specific remote agricultural demand, such water irrigation for fields or cattle. The heart and soul of a solar-powered water pumping system are two solar panels. The solar cell is the smallest component of a photovoltaic panel. Here, we connect the ESP module's outputs to those from sensors installed in the PV panels' controller and water pump. With the use of the GSM module, the main controller unit may show the status of each module, such as whether the motor is on or off, on the administrator's mobile phone. In the event of a motor failure, the malfunctioning component can be identified. Challenges to agricultural land development include transport and road conditions. In this study, we'll look at the numerous options open to the farmer. The primary goal of this study is to develop a solar-powered, Internet of Things (IoT) and GSM-controlled water pump. This reduces the need for human labour (from farmers) in outlying areas. The farmer's mobile device serves as the hub for all activities.

In the banking sector, increased digitization of the economy has forced the businesses to re-evaluate their conventional business models, which is an indication that there is a need for quick and effective change in the banking industry to meet the demands of their clients while offering safe, simple, and connected services to the generation z customers. Industrial revolution 4.0 has affected and changed the banking industry more than 1.0 and 2.0 combined. This paper discusses how the industrial revolution 4.0 has affected and changed the financial and banking sector and primarily revolves around commercial and investment banks rather than reserve banks. It provides a vivid view starting from the digital economy to digitization of banks to the latest trends in the technology incorporated by the banks, to how it has changed the financial markets, to security advancements and finally to how it has changed the banking industry and how it has affected lives.

The consistently expanding traffic, different postponement delicate administrations and energy utilisation compelled prerequisites have carried gigantic difficulties to the ongoing correspondence networks in the transportation framework. Because of the great speed and repeating topological variations of Vehicular Sensor Networks, determining an associated course with sufficient idleness is a difficult task with many requirements and barriers. As a result, in order to combat this, we developed a measurable method for dealing with presumably determining the heap clog and energy utilisation during the lifespan of the sensor network for transportation framework. The paper proposes a Secure and Effective Diffused Framework that spotlights on lower energy use and secure correspondence. The least bounce include in coordinated dissemination is utilised as the rule for developing the slope in this system, which further develops security and dependability by adding the possibility of the angle to flag the course and pace of information transmission.