International Journal on Recent and Innovation Trends in Computing and Communication
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The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
Get PDFThe plastic industry and consumer demand have both exploded since the 1950s. Plastic waste in the ocean has also skyrocketed, growing by a factor of 10 since 1980. Many animal species can't survive this kind of pollution. This is probably bad for people. Impacts plankton, which in turn modifies the carbon cycle. Effects global warming by adding to it. This list, by the way, is not comprehensive. When scarce resources are used inefficiently and without good planning, a great deal of waste is generated, which has a negative impact on the natural world. The notion of a circular economy (CE) has shown encouraging signs of being adopted at industrial and governmental levels as an alternative for the conventional but wasteful linear manufacturing lines. Through careful planning and subsequent reuse, recycling, and remanufacturing, CE strives to maximise the value of raw materials over a product's entire life cycle. Two cutting-edge technologies that can considerably aid in the widespread acceptance and application of CE in actual practises are artificial intelligence (AI) and machine learning (ML). This research delves into how AI applications are being included into CE
Solution of First-Order Differential Equation Using Fourth-Order Runge-Kutta Approach and Adams Bashforth Methods
Get PDFIn this research, we investigate the solution of first-order differential equations (DEs) using Runge- Kutta fourth-order method (RKM) and Adams-Bashforth methods (ABMs). In this work we consider fourth-order RKM and ABMs for solving first order DEs. The method proof to be simple, easy, accurate and efficient technique for solving first order DEs. Moreover, there are unlimited application of fourth-order RK4 and ABMs for solving first-order DE in science, engineering, economics, social science, biology and business. These play an important role in science and engineering. Some examples are giving and solved to support the efficiency of our methods which are demonstrated by figures.
 
Application of Laplace Transform in Science and Engineering
Get PDFOne reliable mathematical tool that is used extensively in many scientific and technical fields is the Laplace transform (LP). Similar to the application of transfer functions in solving ordinary differential equations (ODEs), LPs offer a simple method for tackling increasingly complex engineering problems. LPs are used in physics and engineering, and this research first looks at such uses before concentrating on how they are used in electric circuit analysis. The research also explores more sophisticated uses, such as load frequency control in power systems engineering
Model-Based Testing Approaches using UML Diagrams: A Systematic Literature Review
Get PDFSoftware Unit Testing (SUT) is the starting point for Model-Based Testing (MBT), a testing method. The Unified Modeling Language (UML) has become the standard for modelling software in professional and academic settings. There are various uses for the modelling language known as UML. The findings of an SLR on UML-based model-based testing methodologies are presented in this paper. Thirty-five primary articles about six research issues were examined using selection and exclusion criteria. Methods, model class, intermediate format use, and testing methodology are the primary points of examination. The review outcomes identify future research needs and avenues of inquiry
An Empirical Study of Destructive Nodes Characterization in Wireless Networks
Get PDFMobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs
Collaborative Applications of Internet of Things in various spheres of life: Past, Present and Future
Get PDFThe Internet of Things (IoT) connects and establishes communication between physical objects from creatures to machinery over the Internet without human involvement that is embedded with sensors, actuators, software, and various other technologies linked together through wired or wireless networks. In the foreseeable future, the application fields of the Internet of Things will increase continuously and dramatically. This paper considers the current progress of the Internet of Things in the real world and presents various tangible applications of IoT in field of agriculture, industries, smart retails, automated systems, smart buildings, automotive IoT, wearable items, transportation, covid -19, e-health, security and intrusion detection. The paper also provides overview of the collaborative applications of the Internet of Things with Big Data, Artificial Intelligence, Machine Learning, Wireless Sensor Networks, Cloud Computing, Data Management, Cryptography and Blockchain to disseminate its applications for a better understanding of the research community to apply IoT in further innovative fields
Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs
Get PDFOnline Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity.
This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods.
 
Multiple Sclerosis Classification Using Deep Learning Techniques
Get PDFThe diagnosis of Multiple sclerosis with different types is a big challenge for the doctor and takes more time in real life. We develop two deep learning techniques in order to classify the MS type. The MS has four types: MS-axial, control-axial, MS-sagittal, and control-sagittal. After that, we apply many preprocessing steps to the dataset in order to make it suitable to feed to the classification process like convert the target class label to numeric. We used four evaluation metrics to compare deep learning models: VGG19 and VGG16: recall, f1-score, accuracy, and precision. The results showed that the VGG19 gave better results compared with the VGG19 model in terms of four evaluation metrics of accuracy = 98.6%. The results indicated that we can rely on VGG19 in the classification process for many MS types
Review on Lightweight Cryptography Techniques and Steganography Techniques for IOT Environment
Get PDFIn the modern world, technology has connected to our day-to-day life in different forms. The Internet of Things (IoT) has become an innovative criterion for mass implementations and a part of daily life. However, this rapid growth leads the huge traffic and security problems. There are several challenges arise while deploying IoT. The most common challenges are privacy and security during data transmission. To address these issues, various lightweight cryptography and steganography techniques were introduced. These techniques are helpful in securing the data over the IoT. The hybrid of cryptography and steganography mechanisms provides enhanced security to confidential messages. Any messages can be secured by cryptography or by embedding the messages into any media files, including text, audio, image, and video, using steganography. Hence, this article has provided a detailed review of efficient, lightweight security solutions based on cryptography and steganography and their function over IoT applications. The objective of the paper is to study and analyze various Light weight cryptography techniques and Steganography techniques for IoT. A few works of literature were reviewed in addition to their merits and limitations. Furthermore, the common problems in the reviewed techniques are explained in the discussion section with their parametric comparison. Finally, the future scope to improve IoT security solutions based on lightweight cryptography and steganography is mentioned in the conclusion part
Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study
Get PDFDiabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings