Summary
Technological advancements in the area of the Internet of Things have fostered the development of multi‐hop architectures pertaining to applications seeking large network areas. However, ...while exploiting such applications, the sensor devices being used are made to communicate through multi‐hop routing techniques, burdening the relay nodes. Hence, it leads to a hot‐spot problem, as the nodes passing on the data, that is, relay nodes, consume their energy at a large magnitude. To solve this issue, in this paper, we propose a novel optimized routing technique to mitigate hot‐spot problem (NORTH) for wireless sensor network (WSN)‐based IoT. We employ the tunicate swarm algorithm (TSA) to optimize the cluster‐based routing, specifically the selection of cluster head (CH) of each cluster by using some novel parameters. These parameters include energy status, a distance of a node from the sink and other nodes, load balancing, node proximity, and average energy stock of the network. We investigate two network scenarios, that is, when a sink is placed inside the network and otherwise, to give an optimized solution for every case. Further, to mitigate the hot‐spot problem, the relay node is selected in a cluster with the same mechanism as CH, which performs the task of data forwarding. The simulation analysis of NORTH reveals the supremacy of the proposed work against the recently proposed algorithms, based on various performance metrics, namely, network longevity, stability duration, throughput, and the network's remaining energy.
A novel optimized routing technique to mitigate the hot‐spot problem (NORTH) for WSN‐based IoT is proposed. Two different network scenarios are proposed wherein the CH selection is optimized using tunicate swarm algorithm (TSA). In first scenario, the sink is placed inside the network, and in the second, the sink is placed outside the network and the relay node is used in the latter scenario for data forwarding. The performance validation for NORTH is done against the cluster‐based routing methods.
In the contemporary globalised and dynamic landscape, the phenomenon of innovation transcends territorial limitations. The research paper titled “Designing Beyond Borders” examines the field of ...E-Design and virtual collaboration, investigating their significant contributions to promoting contemporary innovation on a global scale. The proliferation of sophisticated digital technology and the increasing prevalence of remote work have greatly enlarged the conventional boundaries of innovation, resulting in a significant increase in opportunities for creativity, problem-solving, and worldwide collaboration. This study utilises a comprehensive analysis of relevant literature, case studies, and expert interviews to explore the various aspects of E-Design and virtual collaboration. This statement elucidates the profound influence of these approaches on several sectors, encompassing product design, software development, architecture, and healthcare. This study offers useful insights into the techniques and tools that facilitate teams in overcoming geographical boundaries, time zones, and cultural differences to create extraordinary innovation outcomes, through the examination of real-world instances and best practises. the research study titled “Designing Beyond Borders” delves into the complexities and constraints related to EDesign and virtual cooperation. It specifically focuses on the obstacles of establishing trust, ensuring effective communication, and safeguarding intellectual property in the context of a digital environment without geographical boundaries. The text also delves into the psychological and sociological dimensions of virtual teamwork, emphasising the significance of cultivating a sense of belonging and shared purpose among individuals collaborating remotely. The study highlights the significance of E-Design and virtual collaboration as both a reactive measure to global disasters, such as the COVID-19 pandemic, and a proactive approach for fostering sustainable innovation in the long run. In the current day, it is crucial for organisations to possess a comprehensive comprehension of virtual collaboration and E-Design in order to maintain competitiveness and relevance amongst the dynamic digital landscape.
Summary
The rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting ...in recent decades. However, the resource‐constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state‐of‐the‐art routing protocols.
Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol is proposed for green communication in WSN. In CBEEC, advanced nodes form a chain and one of the rotating leader advanced nodes forwards data to the BS. Advanced nodes act as CHs for the entire run to collect data from the normal nodes (cluster members) deployed in their vicinity. Performance evaluation of CBEEC is performed against competitive two‐level energy heterogeneous protocols.
Digital image attestation is powerful regard for the digital rebellion. To fulfill this need, different watermarking methods have been established. Though, it is hard to accomplish a watermarking ...system that is robust and secure. In this paper, one of the spatial domain approaches has been considered for image hiding and text hiding. The technique used in this work is the least-significant bit steganography which is easy to implement and gives a high mean square error and low peak signal to noise ratio. In this approach, one to eight bits of the first component of the pixels in the carrier image is replaced with the most significant bits of the secret data. To see the effect of the given approach in text hiding and image hiding, experimental results has been performed for different target bit-planes.
In recent decades, there has been a notable emphasis on wireless sensor network data collecting due to the growing demand of data-gathering through WSNs as a result of the Internet of Things (IoT) ...popularity. However, attaining the best possible network performance is severely hampered by the resource constraints of sensor devices. This study presents a solution to this problem by proposing the Greening Wireless Sensor Networks, which considers two different energy levels (normal and advanced) of nodes. In this, the count of advanced nodes is calculated rationally and distributed appropriately. Normal nodes, on the other hand, are haphazardly placed next to advanced nodes. Advanced nodes forward data to the base station (BS) using chain-based transmission. This proposed methodology helps the network save a significant amount of energy as a result. A variety of performance criteria are considered to assess the network's performance, and this proposed protocol can outperform popular routing protocols.
Wireless sensor network (WSN) is precisely outlined as a group of exclusively dedicated spatially distributed sensors for recording and processing environmental data like temperature, humidity, wind ...velocity, air density etc. WSN is a propitious technology because of its cost effectiveness, facile deploybility and flexible size. But, because of several reasons sometimes the WSN changes dynamically and it demands various advanced algorithms and at times, redesigning of the network architecture. ML techniques prove to be helpful in coping up with these disruptive changes. Machine learning is a self-learning approach that enables computing machines to learn from their experiences and respond without the requirement of any human trainer or re-programming 1. In this paper, we have compared several ML algorithms that work well for fault detection in WSNs.
A mastery of consumer behaviour analysis and the ability to personalise the customer experience are prerequisites for success in today's cutthroat business climate. In order to improve the customer ...experience and make purchase predictions, this research explores the world of artificial intelligence. Using a systematic three-step technique, this study aims to improve consumer purchase prediction and provide individualised customer experience. The dataset is preprocessed using Enhanced Z-score normalisation first. This will ensure uniformity and mitigate the impact of extreme cases. After a better collection of features is found using Recursive Feature Elimination (RFE), the dataset is optimised for classification tasks. Lastly, state-of-the-art technology is used in the research via the usage of Enhanced Long Short-Term Memory (LSTM) networks for classification and prediction. Better LSTM networks may be able to detect nuanced changes in customer behaviour, resulting in more accurate predictions. Our study aims to contribute to the advancement of AI applications in CRM by following this rigorous process. We want to help companies better their strategy, offer more personalised experiences, and boost the possibility of consumers completing a purchase by delivering relevant insights.
Copy-Move forgery practices are defamatory tampering processes, affecting digital images. Such practices have become much more common nowadays because of the advent of significantly effective image ...processing tools being readily available. In copy-move forgery technique, an area from the original image is copied and pasted over another image area. This technique provides a way to conceal the important details and information in a digital image without leaving much traces behind to observe the tampering or manipulation of the image. Such practices question the authenticity of an image to forensic. In this paper, the techniques are proposed for the detection of cloning or copy-move forgery that involve both block based extraction using Discrete Wavelet Transform (DWT) and feature point extraction based detection technique i.e., Scale Invariant Feature Transform (SIFT) to extract the tampered regions more precisely. The proposed algorithm mainly involves tentacle matching of features of same features, which can be extracted from each of the blocks by calculating the dot products between their respective unit vectors. The proposed algorithm is able to achieve an efficiency of 98.12%, precision factor of 97% of and recall factor of 100%.
Customer - Churn Prediction Using Machine Learning Agarwal, Varsha; Taware, Shwetkranti; Yadav, Suman Avdhesh ...
2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS),
2022-Oct.-10
Conference Proceeding
The gradual but consistent decrease in the number of customers retained over time is referred to as "customer churn," and it is a word that is frequently used in the business and financial sectors. ...If a company can identify the customers who are most likely to leave, they are more likely to take preventative efforts to keep those customers as clients. It is to the bank's advantage to have knowledge about which customers are theoretically and practically most likely to switch banks in the relatively close future. This article explains how to use machine learning algorithms to identify banking customers who may be considering switching financial institutions. This article demonstrates how machine learning models such as Logistic Regression (LR) and Naive Bayes' (NB) can effectively forecast which customers are most likely to leave the bank in the future by using data such as age, location, gender, credit card information, balance, etc. The article also uses data such as age, location, gender, credit card information, balance, etc. In addition, this article demonstrates the probabilistic predictions that may be generated using machine learning models such as Logistic Regression (LR) and Naive Bayes (NB). The findings of this research ultimately point to the conclusion that NB is superior to LR.
With the growing needs of Agriculture sector, the farmers and stakeholders need to make important decisions influenced by various factors like soil type, pollution level, humidity, temperature, ...rainfall, geographic attributes etc. This paper deliberates about the various data mining techniques that analyze the environmental factors that affect the agricultural parameters. These techniques give solution to various decision making problems faced by the agriculture sector today. In this paper we focus on optimizing effect of weather on agriculture using various techniques like Correlation Analysis, multidimensional modeling, k-means, ANN, SVM, KG classification, PAM, CLARA, DBSCAN etc. This information can help our farmers to increase their production based on the behavior of the climate of their location.
