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Business and Environmental Perspectives of Submarine Cables in Global Market
If an individual uses any of the social media networking sites, such as Facebook, Instagram, YouTube, Twitter and the like, a subsea cable is involved there. Submarine cables are considered as critical global communications infrastructure. These cables are used by various telecom providers and content provider companies such as Google, Facebook, and Microsoft to provide seamless transmission of data for their services. Growing internet users and increasing internet traffic for various social media sites is the major reason for the growth of this market. Submarine cables enable data services such as the email, internet banking, social media networking, search engines and all other aspects related to internet that are taken for granted in daily life. These submarine cables scales up the ubiquity of cloud computing and builds digitization of activities. Undersea cable network is the new economic trade route and acts a commodity in Information age. This paper reviews the business and environmental impacts of submarine cables in the global market. Springer Nature Switzerland AG 2020. -
Butterfly Optimization Algorithm-Based Optimal Sizing and Integration of Photovoltaic System in Multi-lateral Distribution Network for Interoperability
In this paper, a new and simple nature-inspired meta-heuristic search algorithm, namely butterfly optimization algorithm (BOA), is proposed for solving the optimal location and sizing of solar photovoltaic (SPV) system. An objective function for distribution loss minimization is formulated and minimized via optimally allocating the SPV system on themain feeder. At the first stage, the computational efficiency of BOA is compared with various other similar works and highlights its superiority in terms of global solution. In thesecond stage, the interoperability requirement of SPV system while determining the location and size of SPV system among multiple laterals in a distribution system is solved without compromises in radiality constraint. Various case studies on standard IEEE 33-bus system have shown the effectiveness of proposed concept of interline-photovoltaic (I-PV) system in improving the distribution system performance in terms of reduced losses and improved voltage profile via redistributing the feeder power flows effectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
ByWalk: Unriddling Blind Overtake Scenario with Frugal Safety System
Safety is crucial, and the truth is ineluctable with its practicality. We strive forward to rev up the safety protocols even more in the field of Road Safety in particular. Countries like India face around 5,00,000 accidents, which lead to 1,80,000 demises each year. The two-lane one-way roads present a risk of the overtaking vehicle crashing onto an incoming car (from the opposite direction) that the overtaking vehicle is unaware of. We seek to achieve two equivocal milestones with our idea in the blind overtake issue, namely, technological aid and economic feasibility. This makes our concept equally impactful in all situations. The technological precision and advancement will help anyone with enough resources to use them tangibly, and economic feasibility ensures a threshold of safety levels that must be put into action. In fact, we are slightly inclined toward the frugality of the architecture paradigm of our idea because safety is everyones right. On the economic side, we propose an LED board-based solution that presents enough information about the incoming vehicle with which a blind overtake condition can be avoided. Besides, we put forward the idea of vehicle-to-vehicle communication for streaming the video content to the trailing cars with smarter selection and added ease to the drivers. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
C-cordial labeling of line signed graphs-I
Let S=(G, ?) be a signed graph. S admits C-cordial labeling if the difference between the number of negative and positive edges (vertices) differ by at most one under canonical marking of S. In this paper, we characterize signed paths and cycles having given number of negative sections where the line signed graphs admit C-cordial labeling. 2020 Author(s). -
Cached-N-Proxy: An Intelligent Proxy Algorithm for Preventing Insider Email Threats to Mail Servers
Insider threats are serious security risks that come from people who work for or are contracted by an organization, such as partners, employees, or contractors. These people use their authorized access to commit hostile acts against the infrastructure, data, or assets of the company. Serious ramifications could result from these dangers, such as financial losses, reputational harm, data breaches, and possible threats to national security. Enterprises must strengthen their defenses with strong intrusion detection and prevention systems because of the growing attack surface for insider threats caused by the increasing adoption of digital technology and remote work habits. Organizations must use a combination of preventive strategies and detection mechanisms, such as privileged access management (PAM), role-based access control, data loss prevention (DLP) techniques, two-factor authentication, and thorough insider threat awareness training, to effectively combat insider threats. 2024 IEEE. -
Calibration of Optimal Trigonometric Probability for Asynchronous Differential Evolution
Parallel optimization and strong exploration are the main features of asynchronous differential evolution (ADE). The population is updated instantly in ADE by replacing the target vector if a better vector is found during the selection operation. This feature of ADE makes it different from differential evolution (DE). With this feature, ADE works asynchronously. In this work, ADE and trigonometric mutation are embedded together to raise the performance of an algorithm. The work finds out the best trigonometric probability value for asynchronous differential evolution. Two values of trigonometric mutation probability (PTMO) are tested to obtain the optimum setting of PTMO. The work presented in this paper is tested over a number of benchmark functions. The benchmark functions results are compared for two values of PTMO and discussed in detail. The proposed work outperforms the competitive algorithms. A nonparametric statistical analysis is also performed to validate the results. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Can Artificial Intelligence Accelerate and Improve New Product Development
Today, AI have successfully set up a good foundation in a broad scope of business processes. Associations including AI for product headway processes have uncovered more huge yields on hypotheses, better viability in their cycles, and effective utilization of resources. A sensible headway framework is paramount for capable product development, especially for complex endeavours. AI thinking is in like manner improving new product development. AI is probably going to experience clients in numerous areas. New yield evolution as in collaboration utilizes its capital and capacities to make another item or work on a current one. Product development is viewed as one among the fundamental cycles for progress, endurance, and recharging of associations, especially for firms in, by the same token, quick-moving or cutthroat business sectors. AI assists people's lives by expanding connections creating and multiplying items that can work with individuals' daily exercises in quite a large number of areas. Consequently, the impact of involving Artificial Intelligence for new developments is to induce things simpler. This paper attempts to outline the acceleration of new product development with the help of artificial intelligence technology. This study addressed the tailored AI in product improvement and product development transformation. Lastly, this article points out how AI accelerates product development and future outlook. 2023 American Institute of Physics Inc.. All rights reserved. -
Cancer Tumor Detection Using Genetic Mutated Data and Machine Learning Models
Early detection of a disease is a crucial task because of unavailability of proper medical facilities. Cancer is one of the critical diseases that needs early detection for survival. A cancer tumor is caused due to thousands of genetic mutations. Understanding the genetic mutations of cancer tumor is a tedious and time-consuming task. A list of genetic variations is analysed manually by a molecular pathologist. The clinical strips of indication are of nine classes, but the classification is still unknown. The objective of this implementation is to suggest a multiclass classifier which classifies the genetic mutations with respect to the clinical signs. The clinical evidences are text-evidences of gene mutations and analysed by Natural Language Processing (NLP). Various machine learning concepts like Naive Bayes, Logistic Regression, Linear Support Vector Machine, Random Forest Classifier applied on the collected dataset which contain the evidence based on genetic mutations and other clinical evidences that pathology or specialists used to classify the gene mutations. The performances of the models are analysed to get the best results. The machine learning models are implemented and analyzed with the help of gene, variance and text features. Based on the variants of gene mutation, the risk of the cancer can be detected and the medications can be prescribed accordingly. 2022 IEEE. -
Capacity Aware Active Monitoring Load Balancing Principle for Private Cloud
Virtual machines (VMs) are the basic compute elements in cloud computing. There are load balancing principles associated with a job scheduler assigns the requests to these computing elements. Deploying an effective load balancing principle enhances better performance that ultimately achieves users satisfaction at the high level. Assigning an equal requests load appropriate to the capacity of the VMs will be a fair principle that can be the objective of any load balancing principle. Active monitoring load balancing principle assigns the requests to a server based on the pre-computed threshold limit. This paper presents a technique for assessing the capacity of the VMs based on a common attribute. This work measures each VMs processing ability as a percentage using the statistical method called Z-score. A threshold is quantified and the requests are proportioned based on this threshold value. Each server is then assigned with the proportioned requests. Suitable experiments were conducted Requests Assignment Simulator (RAS), a customized cloud simulator. The results prove that the performance of the proposed principle is comparatively better than a few load balancing principles. Areas of future extension of this work were also identified. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Captcha-Based Defense Mechanism to Prevent DoS Attacks
The denial of service (DoS) attack, in the current scenario, is more vulnerable to the banking system and online transactions. Conventional mechanism of DoS attacks consumes a lot of bandwidth, and there will always be performance degradation with respect to the traffic in any of the communication networks. As there is an advent over the network bandwidth, in the current era, DoS attacks have been moved from the network to servers and API. An idea has been proposed which is CAPTCHA-based defense, a purely system-based approach. In the normal case, the protection strategy for DDoS attacks can be achieved with the help of many session schedulers. The main advantage is to efficiently avoid the DoS attacks and increase the server speed as well as to avoid congestion and data loss. This is majorly concerned in a wired network to reduce the delays and to avoid congestion during attacks. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Carbon Dioxide Neutralization across the Global Supply Chain
The increased impacts of climatic changes and global warming has led many organizations to adopt green initiatives in several areas of their business processes. Many multinational companies are moving towards reduction of carbon emission across its various operations. Carbon neutrality is the process where steps are taken to achieve net zero carbon dioxide emissions. This article proposes measures to achieve carbon neutrality across the supply chain globally. As part of its sustainability initiative, organizations have decided to reduce carbon consumption across their plants. This calls for estimation of carbon dioxide emissions and reducing the carbon footprint in the entire supply chain process. It also involves gauging Green House CO2 emissions during the transportation process for all TMC regions and Transportation models used by various companies. The main calculations include total CO2 emissions, CO2 Emissions per Ton. Of Goods Transported, CO2 Emissions per Transport Km. These calculations are done based on factors such as Full Truck Load, Less Truck Load, Sea mode of transportation and Air mode of transportation. An analysis is performed on the resulting calculation figures for different modes of transportation such as road, air and sea. The analysis shows that there is an increase in overall CO2e for Air mode of transportation. The least increase in overall Co2 is Sea mode of transportation. Through this analysis, it helps the company to take better decisions regarding the mode of transportation that they need to adopt to achieve carbon neutrality. The Electrochemical Society -
Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique
Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Cataloging of happy facial affect using a radial basis function neural network
The paper entitled "Cataloging of Happy facial Affect using a Radial Basis Function Neural Network" has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human-computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results. 2013 Springer. -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Categorization of artwork images based on painters using CNN
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd. -
Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management
Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE.