Cloud computing is a rapidly expanding area and its success demands adequate resource utilization, high availability, and maximum performance of cloud-based applications and services. This research presents an overall load balancing types existing in cloud computing that covers both static and dynamic methodologies. The study comprehensively examines round-robin, weighted round-robin, least connection, weighted least connection and random allocation algorithms for load balancing. Static load balancing procedures which allocate jobs based on some predefined standards are juxtaposed to their dynamic counterparts that adjust their strategies regarding the actual workloads as well as resource capacities. By investigating these different approaches, this study hopes to have a comprehensive understanding of their advantages, disadvantages and when they are applicable. Furthermore, the paper highlights how advanced technologies together with computational techniques can enhance efficiency in load balancing stressing the significance of adaptability and forward-thinking capabilities in contemporary cloud systems. This invaluable piece of writing will be useful to researchers and developers who are interested in improving the efficiency of Cloud infrastructure through better load balancer solutions.

This study focuses on the increasing global challenge of the need for efficacy assessment of techniques and the critical problem of GHG emissions from agrosystems. It addresses their processes, input variables, and simulation results in different cropping systems to present a comprehensive analysis of four well- known process models: DNDC, DayCent, STICS, and APSIM. These may include model structure, management practices, weather conditions, and the level of data available at the time the simulation was built that influence the model's accuracy in the study. Development of appropriate measurement and reduction tools in agriculture is quite essential because, as the IPCC noted, agriculture plays a vital role in global anthropogenic emissions of greenhouse gases. This study puts forward an integrated hybrid framework, which includes both process-based models and geospatial techniques to increase the precision of emission assessments over varied agricultural systems. The proposed method showed promising improvement in the scalability and robustness of models to enable effective tailoring of mitigation strategies toward sustainable agricultural practices. This paper aims to enhance model accuracy and applicability by offering a methodology focusing on increasing the model's applicability area, unifying the data analysis, and improving the model's algorithms. This project aims to facilitate sustainable food production methods and climate change adaptation by providing better tools and methods to stakeholders by tackling the problem of identifying and managing soil GHG emissions.

The next generation of mobile technology, known as 5G, poses a huge challenge to the existing state of the communications industry since it intends to solve the issues that have plagued the 4G network in its current iteration. This cutting-edge technology enables the establishment of multiple connections all at once and maintains network ubiquity even in settings that involve high levels of mobility or densely populated areas. As a result, smart cities and intelligent transport systems may stand to benefit from its use. 5G will make it possible for the actual Internet of Things and the Internet of Vehicles to become a reality if this plan is implemented. The advent of 5G will herald the beginning of a new era of opportunities for networks and services. It will help in maintaining an increased data rate, reduced latency, huge simultaneous connections, and ubiquity of networks around the world. 5G will also be a crucial enabler for a true Internet of Things because of its capacity to connect a vast number of sensors and actuators while following severe energy efficiency and transmission limits.The Internet of Things (IoT), a new digital communication paradigm, has made it possible for things that are often found in daily life to interact with one another as well as with people [6]. As a result, the objective of the Internet of Things is to expand both the breadth and depth of the Internet. This will be accomplished by making it easier for a wide variety of devices, including vehicles, home appliances, security cameras, industrial actuators, and many more, to interact with one another in an unobtrusive manner. When 5G is applied to the Internet of Things in cities, it may be able to keep track of the total amount of energy that is consumed by all of the city's public services (including lighting, traffic lights, security cameras, and the heating and cooling of public buildings, amongst other things). Municipalities will, among other things, be able to manage their energy resources more effectively if they have access to this information.

Cotton is a crucial crop that has a significant impact on the global economy, and the timing of the harvest is crucial for maximizing the yield and quality of cotton fiber. However, predicting and detecting the harvesting stage of cotton plants is a complex task that requires analyzing various factors such as plant growth, leaf senescence, and boll maturity. Traditional methods for harvesting prediction are labor intensive and time consuming, making it essential to develop efficient and accurate methods. In this paper, we present a novel deep adversarial network (DAN) called CropCycleNet, which combines the features of both convolutional neural networks and generative adversarial networks. The proposed DAN can identify different stages of cotton plant growth, detect diseases, and affect plants to ensure proper removal. We propose Histogram base Gradients Feature Orientation Transform method influences feature descriptors and allows feature-level fusion to improve object recognition accuracy. Experimental validation of CropCycleNet was performed to evaluate the accuracy, precision, recall, and F1 performance metrics at various stages of cotton plant growth. The proposed DAN identified the harvesting stage in cotton fields with 93.27% prediction accuracy, outperforming other existing state-of-the-art methods.

Traffic management is one of the most serious road problems in today’s situation. To manage all the traffic, Traffic signal plays a major role in traffic management. The current traffic signal system or ordinary traffic light signal system is a pre-arranged traffic system. Therefore, these traffic signal systems have been referred to as fixed traffic lights. This pre-arranged traffic is worked on static time i.e. fix time. Therefore, this system can’t change the waiting time to needy vehicle. Thus, the present work has proposed a traffic management system using analyzing the traffic density. The proposed traffic signal management system avoids traffic management problems with the help of Radio-frequency identification (RFID) that usually arise with common traffic management systems. RFID is a tool that provides an effective solution to automatic traffic detection and management. The proposed decided to green or red the traffic signal light based on present vehicle density in a particular direction. If traffic density is low, then the time duration of the green light decreases respectively and if the traffic density is high then the time duration of another side green light is increased and so on. The proposed system has been implemented using microcontroller at mega 16, AVR studio 4, AVR DUDE GUI and USB programmer. To making the traffic automated using RFID is just a step towards making the road transportation system smarter and safer. It is also a small contribution to making the city smart and safe.

Big data analytics (BDA), as an important tool, is now available to companies who are struggling with problems related to sustainability. However, there are not many case studies of BDA in the academic literature, despite the fact that it has the potential to increase the eco-efficiency of manufacturing. This study focuses specifically on the manufacturing sector to investigate the impact of BDA on green innovation (GI), competitive advantage (CA), and environmental performance (EP) in the context of the manufacturing industry. Big data analytics, also known as BDA, is a relatively new field that has emerged as a result of the growth of contemporary computers and their various uses. The relatively new subjects of business data analytics (BDA) and business analytics (BA) have piqued the attention of both working professionals and academics. The purpose of this article is to conduct an analysis of the influence that Big Data has had on four important performance indicators: innovation, competitive advantage, productivity growth, and support with decision-making. Big data may help organizations obtain vital insights about their consumers, products, and operations, despite the fact that it does come with a few negatives. Businesses are in a better position to quickly implement new ideas, provide better service to customers, increase efficiency, make decisions that are better informed, and ultimately outperform their rivals when they have access to data insights. The expansion in both the amount andquality of the data that is now accessible has led to improvements in the capacities of organizations as well as the opening of new doors leading to growth. Businesses are renouncing established practices in favor of new, inventive, and innovative techniques in order to redefine creativity, competitiveness, and productivity. It is vital, for the sake of achieving sustainable objectives, to have an understanding of big data analytics as a critical component of the road map for green innovation, competitive advantage, and ecological performance.

Web Scraping also known as data scraping means extracting the data from a particular website or web page. Extracting the data is useful for data analysis, collecting business data, real time data etc. Webpages contain a lot of information or data which is in unstructured format and cannot be used directly for data analysis and other purpose. So, after extracting the data it is converted into structured format. Extracting the data from web pages is useful but it is also illegal. We can scrap the data which is available publicly but in a limited request. Some websites may explicitly prohibit or limit scraping activities, so it is essential to respect their policies. Before scraping a website, review their terms of service, privacy policy, and any other relevant policies. Without owner's permission we cannot extract the private data from their websites. Selecting the website and conducting web scraping responsibly, we can collect desired data that can be used for various purposes, such as market research, price comparison, trend analysis, or gaining insights into consumer behavior.

Human body contains many organs, but one of the prime organs is the heart, which is a network of vessels which help the blood to flow into our body. Cardiovascular disease is any disturbance in the heart's normal functioning or performance. In this work, closely worked with heart disease prediction and for that, the team will be looking into the heart disease dataset from where work will be derived various perceptions that help us to know the value of each feature and their connections. Also, here the aim is to detect the possibility of a person be affected by a savior heart disease or not. This research paper presents various algorithms of machine learning by which a projection of heart disease can be made and required steps can be taken as early as possible for prevention. This paper consists of five algorithms (SVM, Decision Tree, K-NN, Ensemble Method and ANN) which are applied to the dataset that is publicly available. On applying Ensemble Method (combination of K-NN, Decision Tree, and SVM) on the dataset an accuracy of 89.88% was attained. ANN was able to outperform all the other algorithms with accuracy of 95.33%.

4 G/LTE mobile networks resolved this issue. Strong physical layer and adaptable network design enable high-capacity mobile broadband Internet. Despite this, the prevalence of bandwidth-intensive technologies like virtual reality, augmented reality, and others has grown. In addition, the rising popularity of new services places astrain on mobile infrastructure. Applications requiring high availability and low latency, such Internet-of-Vehicles or communications between vehicles (IoV). With the advent of the new 5G technology with its massive MIMO radio interface, these problems are no longer a concern. Networks protected by software-defined networking (SDN)and NFV have added a new level of flexibility that allows network operators to serve services with very high requirements across several industries. Network operators must increase and diversify their intelligence to fully comprehend the operational environment, user behaviours, and user demands. A further goal is to become(self-) networkable proactively and effectively. This chapter will look at how AI may help us in the modern world. Next-generation mobile networks that are both efficient and adaptable may benefit greatly from machine learning in the 5G era and beyond. The evolution of AI and ML in network applications.

Smart industries use modern technologies such as machine learning and big data to maintain supply chain management and increase productivity but still the main challenge faced during quality control as this might affect the production rate. Smart industries are completely based on supervised learning that enables better inspection and effectively controls the parameter involved in the production process. Smart industries choose the mechanism that improves production and assures maximum quality. The various kernel function is initially used to select and extract a parameter. Support vector machine (SVM) is a supervised learning approach used in manufacturing industries to evaluate quality control. The SVM model uses the kernel function, namely RBF, along with Neural Networks, in identifying the parameter involved in quality management and undergoes the classification process. SVM consists of C-SVM and V-SVM classifier models involved in the classification process and undergoes training to handle the multiple numbers of consequence aroused during manufacturing. The performance of SVM classifiers and RBF NNs is evaluated. Different kernel functions, such as polynomial, linear, sigmoid, RBF, and over-varying gamma coefficient values, are tested in the experimental evaluation concerned with the comparative analysis of the continuous quality control function of the SVM classifier. Experimental results demonstrate the superiority of the SVM classifier in terms of the estimated computational time (88.1%), F1-measure (89.4%), ROC (65%), and accuracy (94.6%). The goal of the proposed model is to monitor the manufacturing process and control fault occurrence.

The new generation of children faces increased vulnerability, particularly due to the rise of remote learning and excessive screen time leading to a sedentary lifestyle also with the concern of diseases like COVID-19, the child's health monitoring and child's immunization status becomes very crucial. These factors contribute to potential mental and physical health issues for the child in future. This research paper deals with this major issue addressing all the major aspects of children's health and it aims to compare it with the project and the previous research and work done on this topic. The data effective technology used in the project like React and mongoDB also helps to scale up the proposed project with much advance and easy-to-use interface and data visualization contributes to a large part of its effectiveness. By doing this, the study aims to provides valuable insights for effective child health monitoring and intervention strategies.

Handwritten Character Recognition (HCR) has gained significant popularity as an Artificial Intelligence tool in today's world. Recognition of a particular character is one of the challenging tasks when every work is done digitally. This technology converts handwritten characters to machine readable or digital form by applying various machine algorithms. Since handwriting and the style of writing a particular character always varies from one person to another person. A person may write some character whose style may differ in size, shape, font and position. An implementation a Handwritten Character recognition technology that uses deep learning algorithm is used. The dataset used in this technology is EMNIST (Extended Modified National Institute of Standards and Technology) dataset. The format of this dataset is of CSV file and it contains handwritten characters, which includes both uppercase and lowercase letters along with digits and various symbols. The dataset used in this model is divided into two parts one is training and other is testing. We have performed an operation for reshaping the image into 28 x 28 pixels so that it can be fitted with Convolutional Neural Network (CNN). During the training process training data is iterated through multiple epochs. The accuracy of the model is measured simultaneously. During the testing process several grayscale images are used to test the trained model and the prediction is made accordingly This model presents the accuracy, potential and high performance of CNN model in prediction of a handwritten character. This system opens up possibilities for its usage in various other applications, as it speeds up our task of analyzing documents and digitization.

Water is the most important commodity for the existence of life. In India, the main sources of water are rainwater(monsoon) that fills the rivers with water and underground water. However, we have seen a steady decline in underground water levels across India at an alarming rate that affects the agriculture and daily water needs of the citizens. This paper suggests a smart ground water recharge system that automatically recharges the ground water after rain falls. The system uses smart IOT devices, solar panels, and a filter to filter out unwanted materials that come along the rainwater. In this paper a fully automatic rainwater harvesting is introduced that collects rainwater and filters out dirt and unwanted materials(trash) that comes along the rain. It also senses the acidic content of rain and filters it out when needed. Later on, the filtered water can be directly recharged in ground, or it can also be collected in artificial pools for irrigation purposes. Polluted water can be very harmful for our environment and food cycle.

Commercial procedure re-engineering is the fundamental reform of corporate procedures to complete affected enhancements in perilous parts like value, production, price, provision, and speediness. Commercial procedure reengineering methods lie at critical downcast venture prices and procedure repetition on a high vast measure. Commercial method re-developing is essential l to the design of commercial procedures to achieve affected enhancements in vital features are as excellence, productivity, price, provision, and speediness. Commercial method re-developing goals at scaling down venture prices also value repetition on an awfully large measure. BPR was a vital management idea from the mid-1980s to the mid-1990s. The idea is usually attributable to Massachusetts Institute of Technology faculty member Michael Hammer and Babson faculty member Thomas Davenport. Hammer and Davenport started as colleagues, performing on an exploration program referred to as PRISM (Partnership for analysis in info Systems Management) Most businesses believe they won't experience a cyber security breach. But if you were being hacked right now, how would you ever know? The fact is that more than a million records were exposed in 12 distinct breaches in 2019 that affected ERP systems in the financial services, telecommunications, retail, education and even medical research industries. Their analysis efforts of security measures, which were sponsored by a number of the most important companies at the time, concerned developing an associate of nursing study model that may facilitate massive corporations’ benefit from recent advances in technology, as well as personal computers and therefore the web.

These days, the Internet of Things (IoT) gets an incredible amount of thought from analysts as it transforms into significant innovation that guarantees a person's life, brings together things, machines and everything. By allowing the exchange of IoT, we talk about frameworks that incorporate objects into reality and sensors are connected or integrated with those components, which are associated to the Internet through wired and external system architecture. IoT sensors can use a variety of organizations, such as RFID, Wi-Fi, Bluetooth Bluetooth and ZigBee, despite allowing a wide range of networks that use multiple fields, GPRS, GSM, LTE and 3G. The IoT powerful object will share data on the location of the objects and the location of individuals, programming frameworks and different machines. In this paper, we take a look at some IoT applications

In recent years, many proposals for electronic payments via open network have been emerged. One of them is Secured Electronic Transaction (SET) protocol advertised through VISA and MASTERCARD that is presently spread all-inclusive. Although SET has numerous advantages over other proposals for straightforwardness and sociability, there seems to be an agreement on relative inefficiency of the protocol. In this paper, we simply give a brief overview of the secure electronic transaction with the help of the cryptography. This paper divide into different two sections in the first section give the brief introduction of the secure electronic transaction, the second section give the overview of the cryptography, third section for legal risk in electronic transaction and finally an overview of the secure transaction with the help of cryptography

In this paper we will demonstrates a flexible and dependable home security system with supplementary security using an Arduino microphone, with the ability to connect via Internet Protocol (IP) through local Wi-Fi for remote access and control by an approved user using the app smartphone. IoTs describe the impression of linking and analyzing real-world events using the Internet. The concept can be implemented in our home to create a smart, safe and automated environment. The proposed service, based on IoT, is intended to create intelligent environments using computer vision and NFC computers, which send an online message to the owner in situation of any damage, if necessary raises alarm. Do not assume the power acquired as a result of this system and such an existing system is that warnings from a Wi-Fi-connected system can be accessed from any phone regardless of its eyes, regardless of whether connect phone and internet. The purpose of this model is to ensure a smart and secure environment using latest technology for our home.

After many years of their appearance, Wireless Sensor Networks (WSNs) remain a dynamic research point due to their wide extend applications in areas, for example, human services, military, observing and reconnaissance frameworks. In many applications, sensor networks are compelled in energy supply and correspondence data transfer capacity. Along these lines, novel strategies to decrease energy wasteful aspects and for effective utilization of the restricted transfer speed assets are fundamental. Such requirements joined with thick organize organization represent a few difficulties to the outline and administration of WSNs and require energy mindfulness at all layers of the systems administration protocol stack. For example, at the Data-Link layer, low obligation cycle Medium Access Control (MAC) protocols exchange off inactivity for energy productive activity. In this paper, we introduce an overview of cutting edge low duty cycle MAC protocols. We first blueprint the design challenges for MAC protocols in WSNs. At that point, we introduce an extensive overview of the most unmistakable and recent popular MAC protocols. These protocols are ordered into synchronous and asynchronous in light of their method of task. At last, the paper feature open research issues in MAC layer for WSNs is been discussed.

In Wireless ad hoc networks the data is transmitted from source to destination using Request-to-Send and Cleat-to-Send (RTS/CTS) mechanism. RTS/CTS reduce packet collisions due to hidden node; solves problem over carrier sense multiple access (CSMA) and hence achieves high throughput. But some additional problem arises like Exposed Node Problem, Masked Node Problem, RTS-induced Problem and CTS-induced Problem. This survey paper represents the modification of RTS/CTS mechanism which improves the throughput and increases the network performance.