A Framework for Dress Code Monitoring System using Transfer Learning from Pre-Trained YOLOv4 Model
CNN; Object Detection; Transfer Learning; YOLOv4
Maintaining a proper dress code in organizations or any environment is very important. It not only imbibes a sense of discipline but also reflects the personality and qualities of people as individuals. To follow this practice, some organizations like educational institutions and a few corporations have made it mandatory for the personnel to maintain proper attire as per their regulations. Manual checks are performed to adhere to the organizations' regulations which becomes tedious and erroneous most of the times. Having an automated system not only saves time but also there is very little scope of mistakes and errors. Taking this into context, the main aim and idea behind the project is to propose a model for detecting the dress code in such workplaces and educational institutions where the attire needs to be regularly monitored. The model detects Business Formals (Blazer, Shirt & Pants) worn by the personnel, for which CNN has been considered, along with YOLOv4, for performing the detection, due to its nature of giving the highest accuracy in comparison to the other object-detection models. Providing the Mean Average Precision of around 81%, it becomes evident that the model performs quite well in performing the detections. 2023 IEEE.
International Conference on Emerging Trends in Engineering and Technology, ICETET, Vol-2023-April
https://doi.org/10.1109/ICETET-SIP58143.2023.10151460 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163751824&doi=10.1109%2fICETET-SIP58143.2023.10151460&partnerID=40&md5=31bc3f02ee6c83de13f4f31ea6b06c2c
Agarwal D., CHRIST (Deemed to Be University), Department of Data Science, Lavasa, India; Gupta P., CHRIST (Deemed to Be University), Department of Data Science, Lavasa, India; Eapen N.G., CHRIST (Deemed to Be University), Department of Data Science, Lavasa, India
Lightweight Anti DDoS Security Tool: Edge Level Filtering in SDN using P4
Edge level filtering; ICMP authentication; P4 programming language; Software Defined Network (SDN) security
Software Defined Network (SDN) which has a promising future in satellite communication was first introduced as the solution to solve problems existing in the traditional network architecture. So far in SDN, mitigation strategies employed hardware installation or software solution which is heavily dependent on SDN controllers. The disadvantage of these approaches is the a) cost for implementation, b) intensive resource usage, and 3) costly optimization strategy necessary to enhance SDN performance. This research aims to fill the gap of the previously seen defense mechanism by enabling edge-level filtering without involving the control plane. By implementing filtering functions in edge switches, it can provide an efficient and effective defense layer in SDN network systems so that SDN switch can become the first line of defense against packet injection attacks. The proposed solution, Lightweight Anti-DDoS Software (LADS) focuses on lightweight workloads and provisioning of effective filtering mechanism to allow SDN switches to drop and block malicious packets sent by attackers. It utilizes Programming Protocol-independent Packet Processors (P4) programming language to create custom functionalities in SDN switches. P4 allows SDN switches to conduct host authentication and malicious packet filtering as well as blacklisting to isolate attackers. Simulation result proves that LADS efficiently manages malicious activities and maintains network performance during attacks at the data plane independent of SDN controller. 2023 IEEE.
Arafune M.; Goswami B.; Kulkarni M.; Venkatachalam N.; Asadollahi S.
2023 5th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICECCT56650.2023.10179747 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166629405&doi=10.1109%2fICECCT56650.2023.10179747&partnerID=40&md5=fa33f16e4bfebf21c085dcab73f792ea
Arafune M., Queensland University of Technology, Brisbane, Australia; Goswami B., Queensland University of Technology, Brisbane, Australia; Kulkarni M., CHRIST (Deemed to Be University), Bangalore, India; Venkatachalam N., Queensland University of Technology, Brisbane, Australia; Asadollahi S., Fujitsu, Brisbane, Australia
Simulation of the Electrical Control Unit (ECU) in Automated Electric Vehicles for Reliability and Safety Using On-Board Sensors and Internet of Things
Electric Vehicles; Electronic Control Units; energy storage system; Hybrid Electric Vehicles
The adaptation of the energy storage system (ESS) with high power and energy density remains a difficulty for electric vehicles (EVs), despite the increasing demand they are experiencing around the world. A lightweight, compact ESS is necessary to deliver the responsive performance and driving range that modern vehicles need. When planning for widespread use of EVs, it's important to give careful attention to the factors of ESS selection, sizing, and administration. One of the most promising future mobility alternatives is the hybrid electric vehicle (HEV), which offers improved fuel economy and lower pollution levels. As a result, one of the most pressing needs is for automakers to develop new technologies for vehicle design that might help lessen emissions and boost economy. The environmental impact of emissions from light-duty cars is growing in tandem with the annual increase in the number of such vehicles on the road. The usage of other modes of transportation, such as ships and planes, is on the rise, but road transportation will always be the most common. Electronic Control Units, or ECUs, have been increasingly commonplace in cars during the past few decades. Vehicle network multicore CPU scheduling is notoriously difficult. This study's findings consist of a straightforward power-sharing control approach for the HESS based on battery and UC, with the goal of extending the battery's useful life in a city environment. 2023 IEEE.
Sunil Kumar M.; Anuradha T.; Srinivas Y.; Santhi Sri T.; Catherine T.J.; Aurelia S.
Sunil Kumar M., School of Computing, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Department of Computer Science and Engineering, AP, Tirupathi, India; Anuradha T., KCG College of Technology, Department of Electrical and Electronics Engineering, Chennai, India; Srinivas Y., Vignana Bharathi Institute of Technology, Department of ECE, Hyderabad, India; Santhi Sri T., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Guntur, India; Catherine T.J., R.M.K. College of Engineering and Technology, Department of EEE, RSM Nagar, Puduvoyal, India; Aurelia S., CHRIST University, Department of Computer Science, Bangalore, India
Exploring Bio Signals for Smart Systems: An Investigation into the Acquisition and Processing Techniques
Bio-signals; Brain Computer Interface; Electrodes; Electrooculogram Analog Digital Instrument; Human Computer Interface; Man Machine Interface
Bio signals play a vital role in terms of communication in the absence of normal communication. Bio signals were automatically evolved from the body whenever any actions took place. There are lots of different types of bio signal based research going on currently from several researchers. Signal acquisition, processing the signals and segmenting the signal were totally different from one technique to another. Placing electrodes and its standard measurements were varied. The signals gathered from each subject may be varied due to their involvement. Each and every trial of signals can generate different patterns. Each and every pattern generated from the activities also has a different meaning. In this study we planned to analyze the basic measurement techniques handled to record the bio signals like Electrooculogram. 2023 IEEE.
International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023 - Proceedings, pp. 690-694.
Sivasakthivel R., Christ (Deemed to Be University), School of Sciences, Department of Computer, Science, Karnataka, Bengaluru, India; Rajagopal M., Christ (Deemed to Be University), School of Business and Management, Department of Lean Operations and Systems, Karnataka, Bengaluru, India
Optimal Disassembly Sequence Generation Using Tool Information Matrix
Contact matrix; Disassembly sequence; Optimal sequence; Recycle; Tool information matrix
Just as the assembly sequence plays an important role in the early part of the product, the disassembly sequence plays an important part in the final stage of the product. The disassembly sequence determines how efficiently the product can be recycled or it can be disassembled for maintenance purposes. In this study, the disassembly sequence is generated using the Tool Information Matrix (TIM) and the contact relations. In this study the feasible sequences are generated using the TIM and contact relations, afterward, the time required is considered as a fitness equation for generating the optimal disassembly sequence. The proposed methodology is applied to 10-part crankshaft assembly to test the performance in generating the optimal disassembly sequences. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Bala Murali G.; Jariwalab A.; Savaliac S.; Kadamd V.; Mahapatra G.S.; Rout A.
Lecture Notes in Mechanical Engineering, pp. 155-164.
Springer Science and Business Media Deutschland GmbH
Bala Murali G., School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India; Jariwalab A., School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India; Savaliac S., School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India; Kadamd V., School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India; Mahapatra G.S., Department of Mathematics, NIT Puducherry, Puducherry, India; Rout A., Department of Mechanical and Automobile Engineering, CHRIST University, Bengaluru, India
Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach
Bitcoin; Dense Neural Network (DNN); Normalization
Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. 2023 IEEE.
Karahyla J.K.; Sharma N.; Chamoli S.; Shirgire A.; Kant R.; Chauhan A.
2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 953-958.
Karahyla J.K., Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University, Human Rights, Haryana,Mullana, Ambala, India; Sharma N., Lovely Professional University, Physical Education, Punjab, Phagwara, India; Chamoli S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Shirgire A., Dr D y Patil Institute of Technology, Department of Civil Engineering, Maharashtra,Pimpri, Pune, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques
Artificial neural network; Decision tree; K-nearest neighbors; Machine learning; Random forest; Supervised; Support vector machine
Drought is a natural phenomenon that puts many lives at risk. Over the last decades, the suicide rate of farmers in the agriculture sector has increased due to drought. Water shortage affects 40% of the world's population and is not to be taken lightly. Therefore, prediction of drought places a significant role in saving millions of lives on this planet. In this research work, six different supervised machine learning (SML) models namely support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent neural networks (RNNs) are compared and analyzed. Three dimensionality reduction techniques principal component analysis (PCA), linear discriminant analysis (LDA), and random forest (RF) are applied to enhance the performance of the SML models. During the experimental process, it is observed that RNN model yielded better accuracy of 88.97% with 11.26% performance enhancement using RF dimensionality reduction technique. The dataset has been modeled using RNN in such a way that each pattern is reliant on the preceding ones. Despite the greater dataset, the RNN model size did not expand, and the weights are observed to be shared between time steps. RNN also employed its internal memory to process the arbitrary series of inputs, which helped it outperform other SML models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Choesang T.; Ryntathiang S.; Jacob B.A.; Krishnan B.; Kokatnoor S.A.
Smart Innovation, Systems and Technologies, Vol-351, pp. 295-307.
Choesang T., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Ryntathiang S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Correlation Based Feature Subset; Personality Prediction; Social Network
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.
Sujatha N.; Pramod S.; Bhatla S.; Thulasimani T.; Kant R.; Chauhan A.
2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 959-964.
Sujatha N., B. M. S. College of Engineering, Department of Mathematics, Karnataka, Bengaluru, India; Pramod S., Nitte Meenakshi Institute of Technology Yelahanka Bangalore, Department of Mathematics, Karnataka, India; Bhatla S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Thulasimani T., Bannari Amman Institute of Technology Sathyamangalam, Department of Mathematics, Tamilnadu, Erode, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
X-Tract: Framework for Flexible Extraction of Features in Chest Radiographs for Disease Diagnosis Using Machine Learning
Diagnostic methods; Feature engineering; Image classification; Pre-built models; Statistical techniques; Support vector machines; Textural descriptors; X-rays
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.
Smart Innovation, Systems and Technologies, Vol-351, pp. 335-349.
Yugandhar C., Department of Computer Science, Christ University, Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bengaluru, 560029, India; Hiremath M., Department of Computer Science, Christ University, Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bengaluru, 560029, India
Performance Analysis of Nonlinear Companding Techniques for PAPR Mitigation in 5G GFDM Systems
Exponential companding; GFDM; High-power amplifier; Mu law companding; PAPR reduction; Root companding
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.
Lecture Notes in Networks and Systems, Vol-689 LNNS, pp. 39-52.
Gopan N.R., Department of ECE, Rajagiri School of Engineering and Technology, Kochi, India; Sujatha S., Department of ECE, Christ (Deemed to be University), Bengaluru, India
Impact of Perceived Social Support on Patient Empowerment: A Study of Online Patient Support Groups
Online health communities; Online patient support groups; Patient empowerment; Perceived social support
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.
Pandey V.; Saha S.K.; Mukherjee S.; Nair S.V.R.; Jha A.
Lecture Notes in Electrical Engineering, Vol-1037 LNEE, pp. 111-117.
Pandey V., Sikkim Manipal Institute of Technology, SMU, Majitar, India; Saha S.K., Christ University, Bangalore, India; Mukherjee S., Sikkim Manipal Institute of Technology, SMU, Majitar, India; Nair S.V.R., Christ University, Bangalore, India; Jha A., Sikkim Manipal Institute of Technology, SMU, Majitar, India
A Systematic Review on the Identification and Classification of Patterns in Microservices
microservice patterns; microservices; pattern classification; pattern identification; software architecture
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.
2023 4th International Conference for Emerging Technology, INCET 2023
Norton Stanley S.A., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Sebastian S., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India
Advancements in Electronic Healthcare: A Bibliometric Analysis
Bibliometric analysis; Blockchain; Electronic healthcare; Internet of things; Patient
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.
Lecture Notes in Electrical Engineering, Vol-1037 LNEE, pp. 101-110.
Saha S.K., School of Business and Management, Christ University, Bengaluru, India
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
CNN; Cnn compression; Crop disease; Deep neural network
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.
Agarwal M.; Singh S.P.; Kaliyar R.; Gupta S.K.; Garg D.; Jindal M.
Communications in Computer and Information Science, Vol-1781 CCIS, pp. 309-320.
Agarwal M., Bennett University, Gr. Noida, 201310, India; Singh S.P., Bennett University, Gr. Noida, 201310, India; Kaliyar R., Bennett University, Gr. Noida, 201310, India; Gupta S.K., Bennett University, Gr. Noida, 201310, India; Garg D., Bennett University, Gr. Noida, 201310, India; Jindal M., Christ Deemed to be University Delhi NCR, Mariam Nagar, Ghaziabad, 201003, India
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN
Dynamic load bal-ancing; HULA mechanism; P4; Programmable Data-Plane; Scalability
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.
Kulkarni M., CHRIST (Deemed to Be University), Bangalore, India; Goswami B., Queensland University of Technology, Brisbane, Australia; Paulose J., CHRIST (Deemed to Be University), Bangalore, India
Rescue Operation with RF Pose Enabled Drones in Earthquake Zones
Cloud Computing; Dijkstra's Algorithms; Disasters; Drones; Earthquakes; EHT Sensor; Internet of Things
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.
Saji A.; Thomas A.R.; Benny A.M.; Jayapandian N.
International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023 - Proceedings, pp. 1333-1338.
https://doi.org/10.1109/ICSCSS57650.2023.10169438 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166265274&doi=10.1109%2fICSCSS57650.2023.10169438&partnerID=40&md5=f73951b607288ac7db9c3bc66e6adf0f
Saji A., Christ University, Department of Cse, Bangalore, India; Thomas A.R., Christ University, Department of Cse, Bangalore, India; Benny A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India
GNSS Signal Obstruction Removal Tool for Evaluating and Improving Position Accuracy in Satellite Networks
azimuth and elevation; GNSS; positioning performance; RTKLIB; Satellite; signal obstruction
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.
https://doi.org/10.1109/ICECCT56650.2023.10179721 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166621958&doi=10.1109%2fICECCT56650.2023.10179721&partnerID=40&md5=b7cad0f33864d4dc86eab69e26a8caf9
Hirota K., Queensland University of Technology, Brisbane, Australia; Goswami B., Queensland University of Technology, Brisbane, Australia; Kulkarni M., CHRIST (Deemed to Be University), Bangalore, India
System Design for Financial and Economic Monitoring Using Big Data Clustering
Big Data; Clustering; Economic; Financial; Monitoring
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.
Madem S.; Katuri P.K.; Anandasubramanian C.P.; Anandasubramanian C.P.; Kalra A.; Singh P.
Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023
https://doi.org/10.1109/ACCAI58221.2023.10200699 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170089277&doi=10.1109%2fACCAI58221.2023.10200699&partnerID=40&md5=c2f93a927d59be579b3ffc2737bfecd0
Madem S., Vignan's Institute of Information Technology (A), Department of Management Studies, Visakhapatnam, 530049, India; Katuri P.K., Vfstr Deemed to Be University, Department of Management Studies, Andhra Pradesh, India; Anandasubramanian C.P., Vels University, Tamil Nadu, Chennai, 600117, India; Anandasubramanian C.P., Christ University, School of Business and Management, Bangalore, India; Kalra A., Mba, Brandeis University, Transfer Pricing and International Economics Subject-Matter Expert, MA, Waltham, United States; Singh P., Lovely Professional University, Department of Management, India
An Innovative Method for Housing Price Prediction using Least Square - SVM
House Price Prediction (HPP); Least Square Support Vector Machine (LSSVM); Normalization
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.
Goel Y.K.; Swaminathen A.N.; Yadav R.; Kanthamma B.; Kant R.; Chauhan A.
2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 928-933.
https://doi.org/10.1109/ICESC57686.2023.10193369 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168314447&doi=10.1109%2fICESC57686.2023.10193369&partnerID=40&md5=66ea8e4736fd4ca708784ace0eae3bc5
Goel Y.K., Surajmal University, Computer Science Engineering, Uttarakhand,(Udham Singh Nagar), Kichha, India; Swaminathen A.N., Adi Shankara Institute of Engineering and Technology, Civil Department, Kerala, kalad, India; Yadav R., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Kanthamma B., Gitam Deemed to Be University, Electrical, Electronics and Communications Engineering (EECE), Andhra Pradesh, Visakhapatnam, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Into the Dark World of User Experience: A Cognitive Walkthrough Study
Cognitive Walkthrough; Dark Patterns; User Experience
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.
Communications in Computer and Information Science, Vol-1832 CCIS, pp. 609-618.
Jaiswal R., CHRIST (Deemed to Be University), Bengaluru, India; Moosath H., CHRIST (Deemed to Be University), Bengaluru, India