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Dynamic job sequencing of converging-diverging conveyor system for manufacturing optimization
Some sectors, such as dairy, automobile, pharmaceutical, computer and electronics, require a range of manufacturing steps to produce a component. The goods in these industries are produced in varieties and the output volume varies from low to high. Typically, these types of businesses use a conveyor system that could have a combination of a diverging and converging conveyor system due to a variety of processing phases involved in the development of the commodity. A conceptual model of the of conveyor system is described, which works manually and to illustrate the importance of the sequence using buffer the buffer layout is modeled and compared to the manual layout. The genetic algorithm is used to find the optimal buffer storage. It can be observed that by adapting various sequencing methods there will be reduction in manufacturing time and setup cost. 2022 Elsevier Ltd. All rights reserved. -
Role of Blockchain in the Healthcare Sector: Challenges, Opportunities and Its Uses in Covid-19 Pandemic
As the world grapples with the Covid-19 pandemic and major populations are getting vaccinated, increasing realisation processes healthcare industry needs to be augmented. It includes managing supply chains, healthcare records, and patient care. With a scarcity of time and resources, adaptation of blockchain technology will help mitigate the pressures on existing infrastructure. A blockchain distributed ledger helps to exchange health information securely without complex intermediation of trust with secure access. The organisations and persons in the blockchain network can verify and authorise the data, thus protecting patient identity, privacy, medical information system, and reducing transaction costs. The paper examines managing and protecting electronic medical records and personal health records data using blockchain. It also analyses issues in healthcare, blockchain implementation, and its uses in the Covid-19 pandemic. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Cloud Computing Application: Research Challenges and Opportunity
In a world with intensive computational services and require optimal solutions, cloud security is a critical concern. As a known fact, the cloud is a diverse field in which data is crucial, and as a result, it invites the dark world to enter and create a virtual menace to businesses, governments, and technology that is facilitated by the cloud. This article addresses the fundamentals of cloud computing, as well as security and threats in various applications. This research study will explore how security is remaining as a potential risk for cloud users across the globe by listing some of the cloud applications. Some viable solutions and security measures that could help us in analyzing cloud security threats are reviewed. The analyzed solutions include profound analytical thinking on how to render the solutions more impactful in each scenario. Several cloud security solutions are available to assist businesses in reducing costs and enhancing security. This study discover that if the risks are taken into consideration without any delay then the matter of solutions gets divided into four pillars, which will assist us in obtaining a more comprehensive knowledge. Visibility, compute-based security, network protection, and lastly identity security are referred as four pillars. 2022 IEEE. -
A Review on Synchronization and Localization of Devices in WSN
Wireless sensor networks are communication networks that deal with sensor devices that are wirelessly interconnected in order to collect and forward data between different environments. Network scaling of small sensor devices with all its limitations has a foolproof scope for future applications. The advantage of minimal infrastructural cost and applicability within challenging environments make it an attractive choice. Statistics have been shown to prove the demand for research for synchronization and localization as a research problem. WSNs are capable of dynamically building virtual infrastructure and getting synchronized with the rhythm of communication setup. Limitations in the amount of energy that can be utilized make it a necessity for the networks to be more optimal in terms of energy consumption. These challenges necessitate the need to study and analyze the recent advancements implemented in approaching synchronization and localization problems. This paper reviews recent research proposals and methodologies to identify related attributes and their relation to the system. A detailed comparative study is conducted to identify relevant patterns that influence the performance of the networks in terms of energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Study of Collaborative Movie Recommendation System
The number of movies available has expanded, making it challenging to select a film that uses current technology to meet users' needs. Following the widespread use of internet services, recommendation systems have become commonplace. The objective for all recommendation systems now is to employ filtering and clustering algorithms to recommend content users are interested in. Suggestions for a media commodity like movies are offered to consumers by locating user profiles of people with comparable likes which makes users' preferences initially determined to allow them to rate movies of their choosing. After a period of use, the recommender system understands the user and offers films that are more likely to receive higher ratings. A comparison study on the existing models helps to understand future scope and improvements for more personalized models for movie recommendation. In comparison to previous models, the MovieLens dataset gives a dependable model that is exact and delivers more customized movie suggestions. In this paper, an approach to do a detailed study and review the user preferences based on item and content of movies has been made to understand the filtering techniques of the collaborative recommendation system to increase accuracy and give highly rated movies as recommendations to the user is carried and based on the results the recommendation system is built with a content-based filtering technique. 2022 IEEE. -
Grading of Red Chilli, Cardamom and Coriander Using Image Processing
Indian cuisine is known for its wide range of spices. Spices are known as the heart and soul of Indian food. Traditionally, categories are identified based on certain chemical technology or with the help of senses gifted to mankind. In this paper, an image processing technique used to extract multiple features is presented to determine the various categories of spices consumed. This proposed work uses different varieties of common Indian spices such as Capsicum annuum (dry red chilli), Elettaria cardamomum (cardamom) and Coriandrum Sativum (coriander). While creating the image dataset, different categories of all spices were taken from southern region of India. Features are extracted from the manually created image dataset, which forms the base for classification. The result obtained using Multilayer Perceptron (MLP), Naive Bayes and Random Forest classifier is found to be optimal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering
As well as setting up the sources, working with editable poly, information in the interior of the swimming pool, using turbo-smooth and symmetry modifier, this procedure of making a 3D swimming pool model is clarified. The lighting the scene and setting up the rendering, the method in which substances are added to the replica is defined. The methods and techniques of rendering are defined, too. The final rendering is the result of multiple images being drawn. The aim of our research work is to create a swimming pool design with enhancing models with materials affect. The shapes used for that are cylinder, sphere, box, plane and splines. The modifiers are editable poly, editable spline and UVW map. Finally, we used a material editor and target lights for enhancing the model. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rendering View of Kitchen Design Using Autodesk 3Ds Max
The method of creating a 3D kitchen design model is clarified, including setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifier. The way materials are introduced to the model which is defined in addition to lighting the environment and setting up the renderer. Rendering methods and procedures are also defined. Multiple images were drawn to create the final rendering. The goal of our research is to produce a kitchen design that uses materials to enhance models. Cylinder, sphere, box, plane, and splines were the shapes employed. Editable poly, editable spline, and UVW map are the modifiers. Finally, we enhanced the model using a material editor and target lighting. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Covid-19 Classification and Detection Model using Deep Learning
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening. 2022 IEEE. -
Effect of fiber types, shape, aspect ratio and volume fraction on properties of geopolymer concrete A review
Researchers have emphasized on sustainable construction with utilization of industrial wastes or byproducts in production of concrete. Geopolymer concrete is one of the popular construction materials which has shown promising results and potential to substitute conventional energy intensive materials such as Portland cement concrete. Further, the use of fibers has shown potential to overcome various deficiencies of geopolymer concrete. However, there are limited studies which explore the benefits of fiber reinforced geopolymer concrete and its applications. The development of fiber reinforced geopolymer concrete is relatively new construction material and has to be experimentally validated in order to increase its usage in the construction industry. As a result, this review paper is an attempt to discuss the effect of shape, type, aspect ratio and volume fraction of fibers on strength and durability properties of geopolymer concrete. From this detailed review it can be concluded that fiber reinforced geopolymer concrete enhances ductile behavior, tensile strength, toughness & energy absorption capacities. 2022 -
Detection of Various Security Threats in IoT and Cloud Computing using Machine Learning
Due to the growth of internet technology, there is a sharp rise in the growth of IoT enabled devices. IoT (Internet of Things) refers to the connection of various embedded devices with limited processing and memory. With the heavy adoption of IoT applications, cloud computing is gaining traction with the ever-increasing demand to process and compute a massive amount of data coming from various devices. Hence, cloud computing and IoT are often related to each other. However, there are two challenges in deploying the IoT and cloud computing frameworks: security and Privacy. This article discusses various types of security threats affecting IoT and cloud computing, and threats are classified using machine learning (ML). ML has gained much momentum in recent years and is applied in various domains. One of the main subdomains of machine learning is used in IoT and cloud security. A machine learning model can be trained with data based on which the model can predict the impending security threats. Popular security techniques to protect IoT devices from hackers are IoT authentication, access control, malware detection, and secure overloading. Supervised learning algorithms can be used to detect malware in the runtime behavior of applications. The malware is detected from network traffic and is labeled based on its suspicious behavior. Post identification of malware, the application data is stored in a database trained via an ML classifier algorithm (KNN or Random Forest). With increased training, the model can identify malware applications with higher accuracy. 2022 IEEE. -
Synthesis and characterization of Poly-Vinyl Alcohol-Alumina composite film: An efficient adsorbent for the removal of Chromium (VI) from water
Composite poly vinyl alcohol-alumina films were synthesized by a novel eco-friendly route in the absence of template. The physico-chemical nature of the synthesized film was studied using different characterization techniques. The poly vinyl alcohol-alumina composite film was found to be an efficient adsorbent for the removal of Chromium (VI) at higher concentrations from water. The preparation conditions were optimized to synthesize an efficient adsorbent film for the removal of chromium. The surface properties, chemical composition and amorphous nature of the film confirmed by different characterisation techniques attributes to the chromium removal efficiency of the film. Poly vinyl alcohol-alumina films are economically cheap, easy to prepare, efficient adsorbent for removal of chromium (VI) eco-friendly in nature and reusable with effortless regeneration methods. 2022 -
Mechanical strength and impact resistance of hybrid fiber reinforced concrete with coconut and polypropylene fibers
This experimental study investigates the mechanical properties and resistance to impact of concrete reinforced with coconut fibers (CF) and polypropylene fibers (PPF). The fiber proportions were decided based on the results obtained from the tests on coconut fiber reinforced concrete (CFRC) and polypropylene fiber reinforced concrete (PPFRC), tested individually. PP fibers of 12 mm and 24 mm of 0.1%, 0.2%, and 0.3% of the volume of concrete were used in PPFRC. Coconut fibers having 50 mm and 75 mm of 0.2%, 0.3%, and 0.4% of the volume of concrete were used in CFRC. Based on test results, PPF (12 mm) and CF (50 mm) were selected for hybrid fiber reinforced concrete (HyFRC). By varying both PPF and CF content, three different proportions with a total fiber content of 0.2% and 0.3% of the volume of concrete were selected. The improvement in strength was observed to be maximum when the total fiber content in the hybrid fiber reinforced concrete was 0.3%. The increase in impact resistance of HyFRC was almost double that of individual FRC and three times that of plain concrete. 2022 -
Negative Domination inNetworks
We introduce s-domination in signed graphs which is based on the number of negative edges between the dominating set and its complement. The s-domination in both the positive and negative homogeneous signed graph will be studied for each value of s. As a special case, the properties of s-domination in sum signed graphs will be analyzed. The maximum value of s for a graph for which the s-domination exists is identified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Machine Learning Entrenched Brain Tumor Recognition Framework
Brain tumor detection plays a significant role in medical image processing. Treatment for patients with brain tumors is primarily dependent on faster detection of these tumors. More rapid detection of brain tumors will help in the improvement of the patient's life chances. Diagnosis of brain tumors by doctors most commonly follow manual segmentation, which is difficult and time-consuming; instead, automatic detection is necessary. Nowadays, automatic detection plays a vital role and can be a solution to detecting brain tumors with better performance. Brain tumor detection using the MRI images method is an essential diagnostic tool for predicting brain tumors; the implementation for these kinds of detection can be done using various machine learning algorithms and methodologies. It helps the doctors understand the actual progression of the evolving tumor, allowing the doctors to decide how the treatment has to be given for that particular patient and measures required to follow up. Therefore, the intention is to create a framework to detect brain tumors in MRI images using a machine learning algorithm and analyze the performance of the brain tumor detection using sensitivity and specificity, which helps us to analyze how well the algorithm has performed in detecting the brain tumors accurately and develop a mobile application framework in which the MRI images can be directly scanned to know whether the cancer is present in a scanned MRI image or not. 2022 IEEE. -
AROSTEV: A Unified Framework to Enhance Secure Routing in IoT Environment
The Internet of Things (IoT) is a global network which collects, process, and analyzes the data. IoT sensors and devices are limited to low memory, power, and processing capabilities. The RPL is a proactive routing protocol which is mainly intended for the IoT. There is a possibility of routing vulnerabilities, which masquerade the data in IoT environment. In order to overcome this problem, a framework called AROSTEV is proposed which comprises of three techniques such as RDAID, RIAIDRPL, and E2V. The primary objective of AROSTEV framework is to detect and mitigate the routing attacks such as rank decreased attack (RDA), rank increased attack (RIA), and rank inconsistency attack (RInA), respectively. Each technique takes the responsibility to progress its activity against the internal routing attacks. This framework can be used to implement the smart city environment. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computational Methods to Predict Suicide Ideation among Adolescents
Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE. -
Grading of Apples Using Multiple Features
Apple is the most demanding food product that has the utmost importance when it comes to drupes. Food is the very basic necessity for our survival. Every new day brings a change, and the demand for a better quality is no greed. Quality food benefits the health of the living beings, and thus, it increases the economic growth of our country. There is a huge possibility that identifying the different varieties of apples is quite a tedious job for these traders and time consuming. Generally, identification is done manually by the very three basic senses: sight, hearing and smell. In the proposed work, an image processing technique is used to differentiate between the varieties of apples such that the manual process can be eliminated. Commercially available seven varieties of apple with various size, shape and color are considered to create database. Apples are purchased from different places across Karnataka, India to create the database. Various spatial and frequency domain based features are extracted from the images of apple. Naive Bayes, Random Forest and Multilayer perceptron (MLP) classifiers are used and got motivating results. An average accuracy of 78.47% is obtained using methods like Fourier Transform and Discrete Cosine Transform. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Kubernetes for Fog Computing - Limitations and Research Scope
With the advances in communications, Internet of Everything has become the order of the day. Every application and its services are connected to the internet and the latency aware applications are greatly dependent on Fog Infrastructure with the cloud as a backbone. With these technologies, orchestration plays an important role in coordinating the services of an application. With multiple services contributing to a single application, the services may be deployed distributed in multiple server. Proper coordination with effective communication between the modules can improve the performance of the application. This paper deals with the need for orchestration, challenges, and tools with respect to edge/fog computing. Our proposed research solution in the area of intelligent pod scheduling is highlighted with the possible areas of research in Microservices for Fog infrastructure. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.