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A Cooperative Global Sequencing Algorithm for Distributed Wireless Sensor Networks
Data gathering is a very fundamental use for wireless sensor networks. The area to be monitored has sensor units distributed. They can tell how much demand there is. Temperature, pressure, humidity, sun rays, and other factors could be involved. The detected data is sent to a centralized device called a sink or just a base station. Networks are frequently distributed in character, meaning that more than one kind of instrument is placed in a particular area. There is only one kind of component in uniform networks. A tree is created and anchored at the sink after the nodes have been distributed. In distributed networks, flawless aggregation is challenging to accomplish. In contrast to uniform networks, nodes may receive and transmit multiple types of packets. Every message should be forwarded by the node to a parent so that it can be combined in order to increase the likelihood of aggregation. As a result, a node might need to choose more than one progenitor. This implies that various parameters should be taken into account while forming trees. We have improved the literature's suggested combined distributed scheduling and tree generation for distributed networks. We discover that the expanded method maximizes aggregation, schedules the network with fewer time slots, and uses less energy. Additionally, it is discovered that distributed networks require more management costs to schedule than uniform networks do. 2023 IEEE. -
Human Resource Management in the Power Industry Using Fuzzy Data Mining Algorithm
Currently, database and information technology's frontier study area is data mining. It is acknowledged as one of the essential technologies with the greatest potential. Numerous technologies with a comparatively high level of technical substance are used in data mining, including artificial intelligence, neural networks, fuzzy theory, and mathematical statistics. The realization is challenging as well. Job satisfaction is one of several factors that cause employees to leave or switch jobs, and it is also closely tied to the organization's human resource management (HRM) procedures. It is continuously difficult and at times beyond the HR office's control to keep their profoundly qualified and talented specialists, yet data mining can assume a part in recognizing those labourers who are probably going to leave an association, permitting the HR division to plan a mediation methodology or search for options. We have analysed the major thoughts, techniques, and calculations of affiliation rule mining innovation in this article. They effectively finished affiliation broadcasting, acknowledged perception, and eventually revealed valuable data when they were coordinated into the human resource management arrangement of schools and colleges. 2023 IEEE. -
On-board Converter for Electric Vehicle Charging using LCLC Resonant Topology
Due to their high efficiency, high power density, and soft switching characteristics, LLC-based AC-DC resonant converters are a great choice for EV chargers. Adding a capacitor across the magnetizing inductance of the LLC resonant architecture (LCLC configuration) enhances efficiency and reduces the need for a larger series inductor. The output DC voltage of the converter is generally regulated using switching frequency control. However, the power factor of the converter varies significantly with the switching frequency. As a result, any fluctuations in load may cause the converter to operate at a lower power factor. This paper proposes a single-stage topology based on the LCLC resonant structure. Zero voltage switching (ZVS) of the IGBTs used in the converter is ensured by the LCLC resonant configuration. Converter have a power factor correction (PFC) stage on the front of the converter to achieve natural power factor correction. Since the PFC stage and the resonant stage are controlled by the same switches, the converter is smaller and less costly. Simulations in MATLAB/Simulink are used to validate the topology. 2023 IEEE. -
An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG. -
Design and Development of Mobile Robot Manipulator for Patient Service During Pandemic Situations
Time and manpower are important constraints for completing large-scale tasks in this rapidly growing civilization. In most of the regular and often carried out works, such as welding, painting, assembly, container filling, and so on, automation is playing a vital part in reducing human effort. One of the key and most commonly performed activities is picking and placing projects from source to destination. Constant monitoring of patient bodily indicators such as temperature, pulse rate, and oxygen level and service of the patients becomes challenging in the current pandemic condition to the nurses and medical staffs. In consideration to this, a mobile robot with an integrated robotic arm has been designed and developed which can be available for service of patients continuously alongside monitoring them in general ward as well as in ICU of hospitals. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm
Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial Intelligence and Machine Learning Combined Security Enhancement Using ENIGMA
Enigma is a relatively new and emerging field that has the potential to bring significant benefits to the way contracts are executed and managed. The integration of Artificial Intelligence (AI) into smart contract technology can automate repetitive tasks, reduce the need for human intervention, improve decision-making, and provide transparency and trust. It can also provide more flexibility, handle more complex tasks, learn from past experiences, have predictive capabilities, and have human oversight and intervention. All these features make Enigma contracts more advanced than traditional smart contracts. AI-powered smart contracts, or Enigma contracts, can also improve contract execution, increase efficiency, facilitate better negotiation, and facilitate automated dispute resolution. However, as the technology is still in its early stages, major challenges and risks can adopted but the need for robust security. The potential for AI is to make decisions that are not in the best interests of its parties. Despite these challenges, the potential benefits of AI-powered smart contracts make them an area of on-going research and development that is worth exploring further. Enigma can be used or applied in various fields, and can be used to secure the sensitive information by applying robust security system. Enigma contract is a AI powered smart contract which is used to automate decision-making processes and improve its efficiency, Enigma as the name suggest it is a complex security network which has the potential to revolutionize the security system by increasing efficiency. 2023 IEEE. -
Into the Dark World of User Experience: A Cognitive Walkthrough Study
In this age of AI, the unison of man and machine is going to be more prominent than ever, thus creating a need to understand the underlying framework that is adopted by app designers and developers from a psychological point of view. Research on the various benefits and harmful effects of user experience design and furthermore developing interventions and regulations to moderate the use of dark strategies in digital tools is the need of the hour. This paper calls for an ethical consideration of designing the experience of users by looking at the unethical practices that exist currently. The purpose of the study was to understand the cognitive, behavioural and affective experience of dark patterns in end users. There is a scarcity in the scientific literature with regard to dark patterns. This paper adopts the methodology of user cognitive walkthrough with 6 participants whose transcripts were analysed using thematic network analyses. The results are presented in the form of a thematic network. A few examples of the themes found are the experience of manipulation in users, rebellious attitudes, and automatic or habitual responses. These findings provide a basis for an in-depth understanding of dark patterns in user experience and provide themes that will help future researchers and designers develop ethical and more enriching user experiences for users. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Innovative Method for Housing Price Prediction using Least Square - SVM
The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM. 2023 IEEE. -
System Design for Financial and Economic Monitoring Using Big Data Clustering
Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive's structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side. 2023 IEEE. -
GNSS Signal Obstruction Removal Tool for Evaluating and Improving Position Accuracy in Satellite Networks
The positioning accuracy of Global Navigation Satellite System (GNSS) is largely affected by the site's surroundings. However, the methods to simulate GNSS signal obstruction and the nature of signal obstruction have not yet been explored fully. In this research, we investigated a way to remove the signals received from a specific region by specifying azimuth and elevation from GNSS observation files and evaluating how the removal of signals affects GNSS positioning accuracy. In addition, we also investigated the signal blockage for buildings of certain dimensions and a mountain. Python was used as a programming language to develop a program for the signal removal. RTKPOST was used for the GNSS data processing, and RTKPLOT was used for the visualisation of processed data and analysis of positioning accuracy. We successfully developed a Python shell script to remove the signals in GNSS data file from specific region by specifying azimuth and elevation. It was also found that removing signals from azimuth 0 to 100 degree and elevation 0 to 30 degree increased the positioning accuracy within a low multipath dataset. However, when the maximum elevation angle was increased to 45 degrees, positioning accuracy degraded, indicating that the signal from certain elevations have a positive or negative impact on positioning accuracy. Further research avenues are explored as an extension of work done here. 2023 IEEE. -
Rescue Operation with RF Pose Enabled Drones in Earthquake Zones
The main objective of this research is to use machine learning algorithms to locate people stranded by an earthquake or other big disasters. Disasters are often unpredictable, they can result in significant economic loss, and the survivors may struggle with despair and other mental health issues. The time, the victim's precise location, the possible condition of the victim, the resources and manpower on hand are the main challenges the rescue team must deal with. This article examines a model that gathers data and, using that data, predicts risk analysis and probability of finding the shortest distance to reach the person in need. Using a drone equipped with RF-pose technology and EHT sensors, it will be able to locate any individuals trapped inside a collapsed structure. To determine the dataset's extreme points and the shortest route to the victim's location by using the Dijkstra's algorithms. The primary aim of this article is to discuss the idea of applying these ML (Machine Learning) algorithms and creating a model that aids in rescuing those trapped beneath collapsed buildings. Devices that are part of the Internet of Things (IoT) have grown in popularity over the past few years as a result of their capacity for data collection and transmission. Particularly in disaster management, search and rescue operations, and other related disciplines, drones have shown to be useful IoT devices. These tools are perfect for emergency response circumstances because they can be utilized to access locations that are hard to get to or too dangerous for humans. Drones with cameras and other sensors can be used in disaster management to gather data in real-time on the severity of the damage caused by earthquakes and other disasters. The afflicted area may be mapped out with their help, and they can also be used to find survivors and spot dangerous places that should be avoided. The rescue operation can then be planned and the resource allocation made more efficient using this information. Drones can be used in search and rescue operations to find and follow people who are stuck or lost. Drones can be equipped with the RF-pose sensors used in the research described in the abstract to assist in locating people who are buried under debris. Thermal camera-equipped drones can also be used to locate people in low-light or night-time conditions by detecting their body heat. The capacity of drones to offer real-time data is one of the benefits particularly disaster management. 2023 IEEE. -
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN
Multi-rooted topologies are used in large-scale networks to provide greater bisectional bandwidth. These topologies efficiently use a higher degree of multipathing, probing, and link utilization. An end-to-end load balancing strategy is required to use the bisection bandwidth effectively. HULA (Hop-by-hop Utilization-aware Load balancing Architecture) monitors congestion to determine the best path to the destination but, needs to be evaluated in terms of scalability. The authors of this paper through artifact research methodologies, stretch the scalability up to 1000 nodes and further evaluate the performance of HULA on software defined network platform over ONOS controller. A detailed investigation on HULA algorithm is analysed and compared with four proficient large-scale load balancing mechanisms including: connection hash, weighted round-robin, Data Plane Devlopment Kit (DPDK) technique, and a Stateless Application-Aware Load-Balancer (SHELL). 2023 IEEE. -
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
In recent years deep learning and machine learning have been widely researched for image based recognition. This research proposes a simplified CNN with 3 layers for classification from 39 classes of crops and their diseases. It also evaluates the performance of pre-trained models such as VGG16 and ResNet50 using transfer learning. Similarly traditional Machine Learning algorithms have been trained and tested on the same dataset. The best accuracy using proposed CNN was 87.67% whereas VGG16 gave best accuracy of 91.51% among Convolution Neural Network models. Similarly Random Forest machine learning method gave best accuracy of 93.02% among Machine Learning models. Since the pre-trained models are having huge size hence in order to deploy these solutions on tiny edge devices compression is done using Whale Optimization. The maximum compesssion was obtained with VGG16 of 88.19% without loss in any performance. It also helped betterment of inference time of 44.13% for proposed CNN, 56.76% for VGG16 and 63.23% for ResNet50. 2023, Springer Nature Switzerland AG. -
Advancements in Electronic Healthcare: A Bibliometric Analysis
Electronic healthcare has changed the traditional form of medical treatment. The integrated approach of interconnected devices had enhanced the process of record keeping and dissemination, benefitting Doctors, patients, and other stakeholders. This study aims to highlight the research carried out in the field of electronic healthcare from the year 2011 to 2020. Metadata of 821 publications from Scopus database was extracted and analyzed. VOS viewer was used to generate the network diagrams and link strengths. It was found that Harvard Medical School and European Commission were the top publication affiliation and funder, respectively. United Stated dominated with the maximum number of publications till 2017 but was surpassed by publications from India from 2018 onwards. Publications inclined toward Internet of things, network security, retrospective study, and authentication toward the end of this decade indicating the shift in trend for the future. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review on the Identification and Classification of Patterns in Microservices
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. 2023 IEEE. -
Impact of Perceived Social Support on Patient Empowerment: A Study of Online Patient Support Groups
Disease-specific online patient support groups have emerged predominantly in last 30years, and these are being visited by a large number of patents. These platforms obviously bring important benefits to the patients visiting them. An important variable is the perceived social support that patients feel they derive while interacting with healthcare providers and fellow patients over there. Patient empowerment is another variable, and which has been found to be a critical factor in overall well-being of patients. How does the perceived social support felt by patients visiting an online patient support group impact their perceived empowerment? This paper explores this question. Research design is associative, and for which the data has been procured online from the patients visiting online patient support groups. The questionnaire comprises of an independent variable (perceived social support) and a dependent variable (patient empowerment). Validated scales have been used. For analysis, a factor analysis was undertaken to reconfirm the validity of the scales. Thereafter, regression equation has been developed to measure the impact. Results show that the model obtained passes the fitness and the independent variable has a significant positive association with patient empowerment. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Analysis of Nonlinear Companding Techniques for PAPR Mitigation in 5G GFDM Systems
Generalized Frequency Division Multiplexing (GFDM) is a 5G waveform contender that offers asynchronous and non-orthogonal data transmission, featuring several advantages, some of them being low latency, reduced out-of-band (OOB) radiation and low adjacent channel leakage ratio. GFDM is a non-orthogonal multicarrier waveform which enables data transmission on a time frequency grid. However, like orthogonal frequency division multiplexing and many other multicarrier systems, high peak-to-average power ratio (PAPR) is one of the main problems in GFDM, which degrades the high-power amplifier (HPA) efficiency and distorts the transmitted signal, thereby affecting the bit error rate (BER) performance of the system. Hence, PAPR reduction is essential for improved system performance and enhanced efficiency. Nonlinear companding techniques are known to be one of the effective low complexity PAPR reduction techniques for multicarrier systems. In this paper, a GFDM system is evaluated using mu law companding, root companding and exponential companding techniques for efficient PAPR reduction. The PAPR and BER graphs are used to evaluate the proposed methods in the presence of an HPA. Simulations show that, out of these three techniques, exponential companding was found to provide a trade-off between the PAPR reduction and BER performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
X-Tract: Framework for Flexible Extraction of Features in Chest Radiographs for Disease Diagnosis Using Machine Learning
Various types of medical images are used as diagnostic tools for identifying pathologies in human bodies, and in this research, chest X-ray images are used as diagnostic tools. Several pre-built models are created by the participants of ImageNet competitions for non-medical images, and these models are also being used in medical image classification; for example, Khan et al. (Comput Methods Prog Biomed 196:105581, 2020) developed a model called Coronet and Narayan Das et al. (IRBM 1:16, 2020) proposed a deep transfer learning-based model. Instead of using the pre-built models, a different approach was taken to address this problem. A framework was created to extract the frequency and spatial domain-based features, along with the raw statistics of the images. The model proposed in this article using the SVM algorithm has reached accuracy levels ranging from 91% to 97% and sensitivity of 92% to 96% on various samples of test data of over 400 images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE.