Unraveling Women's Involvement in the Digital Realm: An Empirical Investigation
cyber security; digital awareness; digital world; women security; women victims
A virtual world in which communication is done through the electronic medium using the computer. This world allows the user to gain knowledge in the form of information. Even though it has a lot of advantages, there are enormous issues when an individual exists in cyberspace. At hand are several challenges to be overcome by individuals to protectively survive cyberspace. Such as various attacks, financial risks, online crimes, and more. In cyberspace, the targeted audience is womanhood of all eons. Educating and promoting awareness about the risk in cyberspace for women in society is the need of the hour. Each individual is facing risk while they are in a digital world. Stakeholders are not given alertness of the threat and its consequences. The paper analyzes the risk and consequences of women's society, as most victims are from that environment. In this, different risks faced and the consequences affected by women's civilization, are discussed. Also remedial measures are taken and should be taken are also deliberated. Supporting this, an online survey is taken from various groups of common people to know the status of women's civilization in the current era. 2023 IEEE.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICCCNT56998.2023.10306918 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179846403&doi=10.1109%2fICCCNT56998.2023.10306918&partnerID=40&md5=9ff452a4ab0cef14770eb7960fef5cb7
Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India
Simulation of IoT-based Smart City of Darwin: Leading Cyber Attacks and Prevention Techniques
Cyber Attacks; Cybersecurity; Internet of Things; Network Simulation; Smart City; Vulnerabilities
The Rise of the Internet of Things (IoT) technology made the world smarter as it has embedded deeply in several application areas such as manufacturing, homes, cities, and health etc. In the developed cities, millions of IoT devices are deployed to enhance the lifestyle of citizens. IoT devices increases the efficiency and productivity with time and cost efficiency in smart cities, on the other hand, also set an attractive often easy targets for cybercriminals by exposing a wide variety of vulnerabilities. Cybersecurity risks, if ignored can results as very high cost to the citizens and management as well. In this research, simulated IoT network of Darwin CBD has been used with different IoT simulation tools. The treacherous effects of vulnerable IoT environment are demonstrated in this research followed by implementation of security measures to avoid the illustrated threats. 2023 IEEE.
Khan S.A.; Sundaram J.; Palendeng M.; Azam S.; Shanmugam B.
Khan S.A., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Sundaram J., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, India; Palendeng M., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Azam S., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Shanmugam B., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia
VLSI Implementation of Area-Error Optimized Compressor-Based Modified Wallace Tree Multiplier
Approximate Multiplier; Mean Error; Wallace Tree
Approximate multiplier designs can improve their energy efficiency and performance with only a slight loss in accuracy by using approximate arithmetic circuits. This method is appropriate for applications where an approximative answer is acceptable because it uses a range of calculation approaches to those priorities, returning a potentially erroneous result above one that is assured to be exact. The basic idea underlying approximate computing is that, while accurate calculation may require a lot of resources, bounded approximation can result in considerable speed and energy efficiency advantages without sacrificing accuracy. The approximate 4:2 compressor and exact compressors, as well as half adders and full adders, make up the proposed approximate multiplier. The steps of the multiplier architecture are optimised using the recently suggested modified Wallace Tree Multiplier Architecture. When compared to previous designs, the proposed multiplier architecture can generate outcomes with the least amount of inaccuracy. The multiplier architecture is also finished in just two steps. The Modified Wallace Tree Architecture used in the suggested approximate multiplier excels by providing an error rate of 71.80% and a mean error of 173.82. As a result, the mean ? error Product improved by 10%, the error rate improved by 23.3%, and the mean error increased by 31.04%. This is accomplished by the proposed approximate multiplier with a small increase of 22.36% in total power consumption. 2023 IEEE.
Balamanikandan A.; Suresh K.; Gnanaprakasam D.; Saranya K.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 353-358.
Balamanikandan A., Sree Vidyanikethan Engineering College, Department of ECE, Tirupati, India; Suresh K., Christ Deemed to Be University, Department of EEE, Bangalore, India; Gnanaprakasam D., Dr.Mahalingam College of Technology, Department of EEE, Pollachi, India; Saranya K., Dr.Mahalingam College of Technology, Department of EEE, Pollachi, India
Construction of Virtual Simulation Practice Teaching Platform for Business Majors Based on Fuzzy Control Algorithm
Fuzzy control; Teaching platform; Virtual simulation
Simulation plays an important role in control research. Information technology and various related technologies, the research of simulation technology is also deepening. At present, there is no unified platform for the design and simulation of adaptive fuzzy controller, and the simulation algorithms of various controllers are different. With the strong advocacy of national education departments, virtual simulation technology has been widely used in academic education, and has gradually become an important means to improve traditional teaching. Cross-professional comprehensive training of business has almost become the preferred course of combining theory with practice in general colleges and universities. It requires students from different majors to participate together, cooperate and communicate deeply in teams, and compete and confront each other among groups, which helps to improve graduates' innovative and entrepreneurial ability. Through teaching practice, the design of teaching system, the joint training between schools and enterprises, and the consideration of virtual and actual combat are further improved. Explain the teaching application of virtual simulation experiment teaching platform. The virtual simulation experiment teaching platform is convenient for students to complete intelligent control experiments, and carry out secondary development and innovative experiments. 2023 IEEE.
David Winster Praveenraj D.; Stanley Vincent G.; Jasim L.H.; Sankar Ganesh S.; Kavitha P.; Sridharan M.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1433-1438.
https://doi.org/10.1109/SmartTechCon57526.2023.10391365 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184823564&doi=10.1109%2fSmartTechCon57526.2023.10391365&partnerID=40&md5=044d550888552c6e703a43d51bcb3de2
David Winster Praveenraj D., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Stanley Vincent G., School of Management Studies, Mount Carmel College Autonomous, Bengaluru, India; Jasim L.H., College of technical engineering, The Islamic university, Najaf, Iraq; Sankar Ganesh S., Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Ghanpur Village, Ghatesar(M), Malkajgiri District,Telangana State, Medchal, 501301, India; Kavitha P., New Prince Shri Bhavani College Of Engineering And Technology, Chennai, India; Sridharan M., Department of Mathematics, N.P.R College of Engineering and Technology, Dindigul Dist, Natham, India
An Energy Optimized Clustering approach for Communication in Vehicular Cloud Systems
dragonfly method; energy optimization; heuristics; routing; throughput; Vehicular cloud
Vehicular cloud networks are considered to possess faster transitional topology and mobility thereby adhering to its features as an ad hoc network. Many times, it is difficult to monitor vehicular nodes that results in internetworking concerns as a result of power inadequacy during real computation. This leads to lots of energy wastage issues encountered during routing which degrades lifetime of nodes. Thus in this study a new clustering based energy optimization method is proposed to enhance the efficiency of vehicular communication. K-medoid cluster analysis along with dragonfly approach is applied to the system model to optimize energy. On the basis of simulation undertaken, it is recorded that the network lifetime, packets delivered, processing delay and throughput are increased using the proposed model. 2023 IEEE.
Bhaktisudha S.; Mishra S.; Nandi A.; Sharma V.; Alkhayyat A.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1060-1065.
https://doi.org/10.1109/SmartTechCon57526.2023.10391821 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184808683&doi=10.1109%2fSmartTechCon57526.2023.10391821&partnerID=40&md5=d5cafaa69ff95d7ae937d7dc4e0fbb08
Bhaktisudha S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Nandi A., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST(Deemed to be University), Delhi-NCR, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images
Cancer Detection; CNN; computer aided diagnosis; Deep Learning; Histopathological Images; image Classification; machine learning
Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE.
Ramesh M.; Maheswaran S.; Theivanayaki S.; Kodeeswari K.; Krishnasamy L.; Sriram N.
https://doi.org/10.1109/ICCCNT56998.2023.10307852 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&doi=10.1109%2fICCCNT56998.2023.10307852&partnerID=40&md5=e0adb3b1e5b898cf056da480adc4a970
Ramesh M., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Theivanayaki S., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Kodeeswari K., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Krishnasamy L., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Sriram N., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India
Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution
Decision Tree; Deep Learning; Disease Magnitude; Multi-classification; Wheat Diseases; Yellow Rust
Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security. 2023 IEEE.
Konidena A.; Shanbhog M.; Singh S.; Sharma V.; Jain A.K.; Sharma N.
Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 648-654.
https://doi.org/10.1109/ICTACS59847.2023.10389846 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185374849&doi=10.1109%2fICTACS59847.2023.10389846&partnerID=40&md5=91d61b110abae6d16ce9423ee3d252e7
Konidena A., Iilm University, Department of Cse, Uttar Pradesh, Greater Noida, India; Shanbhog M., Christ University, Department of Computer Science, Uttar Pradesh, Ghaziabad, India; Singh S., School of Computing MIT-ADTU, Department of Information Technology, Pune, India; Sharma V., Graphic Era Hill University, Uttarakhand, Dehradun, India; Jain A.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sharma N., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
SVM Based AutoEncoder for Detecting Dementia in Young Adults
Dementia detection; Explainability; Explainable AI; LIME; SHAP
Dementia's impact on cognitive function necessitates timely diagnosis for effective intervention. Understanding the need for timely detection, the proposed work integrates SVM's decision boundary determination and autoencoder's noise reduction capabilities. The proposed work advances in dementia detection in young adult. Results indicate promising performance, with the model achieving high accuracy around 85.33%. The ROC curve illustrates a balanced trade-off between sensitivity and specificity, while the precision-recall curve highlights effective classification. Importantly, the model surpasses existing literature, underscoring its practical utility. While acknowledging limitations, such as parameter fine-tuning, this study lays the groundwork for refining and expanding this innovative methodology. In summary, this research contributes to the urgent field of early dementia detection, potentially transforming patient care and intervention strategies. 2023 IEEE.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 2516-2520.
Sharma V., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia, CHRIST (Deemed to Be University), Delhi-NCR, India; Midhunchakkaravarthy D., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants
Academic Attributes; Economic Indicators; Graduate; Machine Learning; Student Well Being
In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE.
Basha M.S.A.; Biswas P.; Christina S.; Krishnan D.R.; Martha Sucharitha M.
Proceedings of NKCon 2023 - 2nd IEEE North Karnataka Subsection Flagship International Conference
Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India; Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Design and analysis of single stage Step-up converter for Photovoltaic applications
Boost chopper; Buck chopper; Inverter; Photovoltaic-grid integration; Rectifier operation
Main novelty of the proposed work is dual leg single stage DC-AC converter for DC\AC grid and solar based applications. Operating principles, components design and modulation techniques are presented. Initially proposed concept is simulated in MATLAB Simulink platform and after validated in a real time prototype model is the future work. Proposed idea has some advantages like few passive components, less leakage current due to few switching frequency components, wide range voltage with absence of DC link capacitor. High efficiency due to single stage operation so this circuit is highly suitable for high\low voltage photo-voltaic energy conversion. Electromagnetic interference also less with continuous current. 2023 IEEE.
Krishnan R.R.; David M.J.; Royesee J.M.; Vikas G.M.; Suresh K.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 326-330.
Krishnan R.R., Christ Deemed to Be University, Department of EEE, Bangalore, India; David M.J., Christ Deemed to Be University, Department of EEE, Bangalore, India; Royesee J.M., Christ Deemed to Be University, Department of EEE, Bangalore, India; Vikas G.M., Christ Deemed to Be University, Department of EEE, Bangalore, India; Suresh K., Christ Deemed to Be University, Department of EEE, Bangalore, India
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
Efficiency; multilevel inverter; pulse width modulation; reduced switches; THD
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE.
Parimalasundar E.; Cheepati K.R.; Kamalkumar V.; Suresh K.; Devaraju T.; Kumar B.H.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 1-6.
https://doi.org/10.1109/TEECCON59234.2023.10335776 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181532747&doi=10.1109%2fTEECCON59234.2023.10335776&partnerID=40&md5=4fdfea49f1ac8e13cc3e67f88db17c2c
Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Cheepati K.R., Kandula Sreenivasa Reddy Memorial College of Engineering, Electrical and Electronics Engineering, Kadapa, India; Kamalkumar V., KPR Institute of Engineering and Technology Coimbatore, Electrical and Electronics Engineering, Coimbatore, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India; Devaraju T., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Kumar B.H., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India
Data Ingestion - Cloud based Ingestion Analysis using NiFi
Cloud Ingestion; Data Ingestion; File Ingestion; Ingestion Analysis using NiFi; Ingestion Challenges; NiFi Architecture
Data Ingestion has been an integral part of Data Analysis. Bringing the data from various heterogeneous sources to one common place and ensuring the data is captured in the appropriate format is the key for performing any Big data task. Data ingestion is performed using multiple frameworks across the industry and they all have their own set of benefits and drawbacks. Apache NiFi is one popular ingestion framework which is used widely and does Ingestion effectively. Ingestion is performed on various sources and the data is generally stored in clusters or cloud storage. In this paper, we have done the File Data Ingestion using the NiFi framework on a local machine and then on two cloud-based platforms, namely Google Cloud Platform (GCP) and Amazon Web Services (AWS). The objective is to understand the latency and performance of the NiFi tool on Cloud-based Ingestion and provide a comparative study against the typical Data Ingestion. The entire setup was done on a local machine and two corresponding cloud platforms namely GCP and AWS. The findings from the comparative analysis have been compiled in a tabular format and graphs are created for easy reference. The paper places emphasis on the significance of NiFi's data ingestion performance on Cloud Platform and attempts to present it as a major activity on the data ingestion platform for Cloud Ingestion Solution. 2023 IEEE.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 1099-1107.
Irfan M., Christ (Deemed to Be University), Bangalore, India; Reena, Christ (Deemed to Be University), Bangalore, India; George J., Christ (Deemed to Be University), Bangalore, India
Improved tweets in English text classification by LSTM neural network
English tweets; LSTM; Mendeley Data; Neural network; Word2Vec
This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used. 2023 IEEE.
Yashasvi B.N.; Samson Lsaac J.; Raghavendra S.; Tiwari M.; Ananthi K.
2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023
https://doi.org/10.1109/ICAEECI58247.2023.10370893 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183557680&doi=10.1109%2fICAEECI58247.2023.10370893&partnerID=40&md5=dbec87fe8b9fff9c18cc6389e2f6ad72
Yashasvi B.N., R N S Institute of Technology, Bangalore, India; Samson Lsaac J., Karunya Lnstitute of Technology and Sciences, Department of Biomedical Engineering, Tamilnadu, Coimbatore, India; Raghavendra S., Christ Deemed to Be University, Department of Computer Science Engineering, Benguluru, India; Tiwari M., Bharati Vidyapeeth's College of Engineering, Department of Computer Science and Engineering, Delhi, India; Ananthi K., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Tamilnadu, Kinathukadavu, India
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
Auxiliary Power Unit (APU); Fuel Cell Systems (FCS); Stacked Residual Gated Recurrent Unit Convolutional Neural Networks (S R-GRU-CNN)
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE.
Bansod P.J.; Mohan U.; Yadav D.K.; Singh Y.; Sharmila P.; Chauhan A.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 811-816.
Bansod P.J., Department of Mechanical Engineering, G H Raisoni College of Engineering &Management, Wagholi, Pune, India; Mohan U., Department of Mechanical Engineering, University of Technology and Applied Sciences, Salalah, Oman; Yadav D.K., Department of Electronics & Communication Engineering, IIMT College of Engineering, Greater Noida, India; Singh Y., India Meteorological Department, Mausam Bhawan, Lodhi Road, New Delhi, India; Sharmila P., Department of Science & Humanities, Prince Shri Venkateshwara Padmavathy Engineering College, Tamil Nadu, Chennai, India; Chauhan A., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be university), Karnataka, Bengaluru, India
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
Battle Royale Optimization; BRO; COCOMO model; cost drivers; cost estimation; machine learning; QEMRT; Quantum Ensemble Meta-Regression Technique; software
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE.
Shoran P.; Sinha A.; Mahmood H.R.; Sharma V.; Jha P.; Kumar B.; Alkhayyat A.
https://doi.org/10.1109/ICCCNT56998.2023.10307113 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179851350&doi=10.1109%2fICCCNT56998.2023.10307113&partnerID=40&md5=fe37e1fdafcf79f36c4c54068f74e349
Shoran P., Dr. Akhilesh das Gupta Institure of Technology and Management, India; Sinha A., Ignou, Department of Cs and It, New Delhi, India; Mahmood H.R., Fast Nuces Cfd Campus, Punjab, Pakistan; Sharma V., Christ University, Department of Computer Science, Delhi NCR, India; Jha P., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Kumar B., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
Demand Forecasting; Electronic Ticketing Machines (ETMs); Long Short-Term Memory (LSTM)
Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. 2023 IEEE.
Balaji V.; Anand T.; Abid T.; Ramya D.; Chauhan A.; Hazela B.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 920-925.
Balaji V., Aditya Engineering College, Department of Eee, Andhra Pradesh, Surampalem, India; Anand T., Graphic Era Deemed to Be University, Department of Humanities and Social Sciences, Dehradun, India; Abid T., Nawab Shah Alam Khan College of Engineering and Technology, Department of It, Telangana, Hyderabad, India; Ramya D., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), School of Sciences, Department of Life Sciences, Karnataka, Bengaluru, India; Hazela B., Amity University, Amity School of Engineering and Technology Lucknow, Uttar Pradesh, India
XGBoost Classification of XAI based LIME and SHAP for Detecting Dementia in Young Adults
As technology progresses on a fast pace, it is imperative that shall be used in the field of medicine for the early detection and diagnostics of dementia. Dementia affects humans by deteriorating the cognitive functions, and as such many algorithms have been used in the detection of the same but all these algorithms remain a black box to the medical fraternity which is still dubious about the nature and credibility of the prediction. To ease this issue, the use of explainable artificial intelligence has been proposed and implemented in this paper, which makes it easy to understand why and how the model is giving a particular output. In this paper the XGBoost classification algorithm has been used which give an accuracy of 93.33% and to understand these predictions, two separate algorithms namely Local Interpretable Model-agnostic Explanations (LIME) and Shapely Additive Explanations (SHAP) have been used. These algorithms are compared based on the type of explanation they provide for the same input and thus the weakness of LIME algorithm has been found out at certain intervals based on the clinically important features of the dataset. On the other hand, both the algorithms make it easy for medical practitioners to understand the dominating factors of a predicted output thereby helping to eliminate the black-box nature of dementia detection. 2023 IEEE.
Sharma V., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia, Christ (Deemed to Be University), Delhi-NCR, India; Midhunchakkaravarthy D., Lincoln University College, Department of Computer Science and Multimedia, Kuala Lumpur, Malaysia
Smell Technology: Advancements and Prospects in Digital Scent Technology and Fragrance Algorithms
fragrance algorithm; innovation; Scent technology; smell technology; virtual reality
Smell technology, a rapidly expanding sector of the scent business, aims to digitally replicate and transmit aromas. Its applications include virtual reality, e-commerce, and healthcare. Recent advances in the field include the creation of smell algorithms and the use of artificial intelligence to create more realistic fragrances. Fragrance algorithms are mathematical models that predict the scent of a fragrance based on its chemical composition. They might be used to the perfume industry to streamline the production of perfumes and do away with the need for expensive trial-and-error methods. Artificial intelligence is also being used to create digital representations of fragrances that closely resemble the real thing by analysing the chemical composition of actual odours. A possible benefit of this technology in the healthcare sector is that synthetic odours may mimic the scents of diseases and aid physicians in making more precise diagnoses. Additionally, some companies are developing small devices that can be connected to computers or mobile devices to emit odours on demand, providing users of virtual reality, gaming, and online shopping with a more realistic experience. In spite of these advancements, it is still exceedingly challenging to recreate the complexity of natural scents, which can include hundreds of different components. With more research and development, there is still a tonne of promise for fragrance technology in the future. 2023 IEEE.
Sinha A.; Sharma V.; Gupta M.; Rao G.M.; Raj A.; Kumari A.; Alkhayyat A.
Sinha A., Ignou, Department of Computer Science and Information Technology, New Delhi, India; Sharma V., Christ University, Institute of Information Technology, India; Gupta M., Rungta College of Engineering and Technology, Department of Physics, CG, Bhilia, India; Rao G.M., Koneru Lakshmaiah Education Foundation, India; Raj A., Galgotias University, School of Computing Science and Engineering, Greater Noida, India; Kumari A., Rkdf University, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
A Predictive Modelling of Factors Influencing Job Satisfaction Through a CNN-BiGRU Algorithm
Bidirectional Gating Recurrent Unit (BiGRU); Convolutional Neural Network (CNN); Long short Term Memory (LSTM)
The fields of humanities, psychology, and sociology are where the word 'job satisfaction' originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models. 2023 IEEE.
Kar R.; Renjith Krishnan K.; Antony P.; Rashmi B.H.; Padmapriya S.; Unni M.V.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 68-73.
Kar R., St. Claret College, Department of Management, Bangalore, India; Renjith Krishnan K., College of Economics and Business Administration, University of Technology and Applied Sciences, Shinas, Oman; Antony P., Jyoti Nivas College Autonomous, Department of Mba, Bangalore, India; Rashmi B.H., School of Business and Management, Christ University, Bangalore, India; Padmapriya S., Christ University, Department of Commerce, Bangalore, India; Unni M.V., St. Claret College, Department of Management, Bangalore, India
Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Environmentally Positive Behavior(EPB); Job S atisfaction; Organizational Citizenship Behavior (OCB)
Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE.
Shukla T.D.; Giri P.; Rana P.; Krishna V.P.; Thulasimani T.; Vanisri S.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 771-776.
https://doi.org/10.1109/ICACRS58579.2023.10404165 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185383619&doi=10.1109%2fICACRS58579.2023.10404165&partnerID=40&md5=8c532fa188bb9a8b74c8b1c708ef2b16
Shukla T.D., University Institute of Teachers Training and Research, Chandigarh University, Mohali, India; Giri P., School of Business and Management, Christ (Deemed to be University,), Delhi NCR, India; Rana P., Department of MBA, School of Commerce and Management, IIMT University, Meerut, India; Krishna V.P., School of Management and Commerce, Malla Reddy University, Hyderabad, India; Thulasimani T., Department of Mathematics, Bannari Amman Institute of Technology, Tamilnadu, India; Vanisri S., Department of MCA, M.Kumarasamy College of Engineering, TamilNadu, Karur, India