Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance
autoscaling; cluster autoscaler; container scaling; hpa; kubernetes; performance; scaling; vpa
Kubernetes is an open-source production-grade container orchestration platform, that can enable high availability and scalability for various types of workloads. Maximizing the performance and reducing the cost are two major challenges modern applications encounter. To achieve this, resource management and proactively deploying resources to meet specific application requirements becomes utmost important. Adopting Kubernetes auto-scaler to fit one's needs are important to maximize the performance. This study aims to perform a comprehensive analysis of Kubernetes auto-scaling policies. This paper also lists out the various parameters considered for auto-scaling, and prediction methods used to efficiently handle resource requirements of applications. The research findings reveal a scarcity in the existing work regarding the variety of workload based auto-scaling and custom metrics. This paper provides a concise overview of a forthcoming research endeavor that explores the utilization of custom metrics in conjunction with auto-scaling. 2023 IEEE.
2023 Global Conference on Information Technologies and Communications, GCITC 2023
Institute of Electrical and Electronics Engineers Inc.
Joyce J.E., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Sebastian S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
best-worst method; cobot; mcdm mechniques; robotic assembly
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE.
Mahanta G.B.; Mohanty B.; Rout A.; Biswal B.B.; Jha A.
2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023
Mahanta G.B., Amrita School of Engineering Amrita Viswavidyapeetham, Department of Mechanical Engineering, Chennai, India; Mohanty B., National Institute of Technology, Meghalaya, Department of Mechanical Engineering, Meghalaya, India; Rout A., Christ University, Department of Mechanical Engineering, Bangalore, India; Biswal B.B., Nit Rourkela, Department of Industrial Design, Odisha, India; Jha A., Amrita School of Engineering Amrita Viswavidyapeetham, Department of Mechanical Engineering, Chennai, India
Interpreting Scope of Predictive Analytics in Advanced Driving Assistant System
Advance Driver Assistance System; Automotive; Machine Learning
Distracted driving, caused by various factors such as human emotions or reading distracting messages on the roadside, has become a leading cause of traffic accidents today. Ensuring the safety of both individuals and vehicles while minimizing maintenance costs poses a significant challenge for the automotive industry. Fortunately, recent advancements in machine learning offer a potential solution. One promising method is the further development of Advanced Driver Assistance Systems (ADAS), for which machine learning serves as an ideal solution. The proposed model develops an advanced predictive learning enabled driving assistance system with prediction capabilities like traffic light behavior and parking availability detection. The model gave an optimum accuracy of 98.2% with 50 epochs count and the validation loss retains a constant value of 0.3 over epochs. 2023 IEEE.
Saha R.; Debray A.; Sharma V.; Mishra S.; Al-Khasawneh M.A.; Alkhayyat A.
2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 561-566.
https://doi.org/10.1109/ICAICCIT60255.2023.10465864 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189878297&doi=10.1109%2fICAICCIT60255.2023.10465864&partnerID=40&md5=2f4a964ba096c38dc4068e5218d91fd5
Saha R., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Debray A., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, School of Computing, Sharjah, 1797, United Arab Emirates; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
Smart Product Packing and IoT Marketing: Enhancing Customer Interaction
Competitive advantage; Consumer empowerment; Customer engagement; Customer interaction; Data-driven decision-making; Ethical considerations; IoT marketing; Quantitative analysis; Smart product packaging; Sustainability
The convergence of smart product packaging and IoT marketing has transformed commerce. This study examines the fundamental ramifications of convergence and its potential to improve customer engagement. Our research shows the transformational potential of these technologies via quantitative and qualitative analyses.Smart packaging outperforms non-smart items, giving firms an advantage, according to quantitative data. Regression and correlation analysis confirm IoT data-customer interaction. Our study also emphasizes ethical data acquisition, which supports data privacy and consumer protection.Consumers may expect personalized experiences, transparency, and real-time feedback from this technology transformation. Smart product packaging and IoT marketing enable readers to make educated decisions and influence product development to meet changing consumer expectations.This research allows academics to study the ideas and models that affect consumer engagement. Data privacy and consumer protection may inform IoT marketing and smart device packaging policy.Our research guides organizations and customers towards better customer interactions, data-driven decision-making, and ethical data practices in this changing age. The future promises revolutionary customer contact. 2023 IEEE.
Soundarraj P.; Aarif M.; Gangadharan S.; Naqvi S.R.; Assihalaf N.K.; Salih Mahdi A.
Proceedings of the 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023
https://doi.org/10.1109/ICSES60034.2023.10465408 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190552055&doi=10.1109%2fICSES60034.2023.10465408&partnerID=40&md5=8f5daecb65c63bebae5212496e0271f9
Soundarraj P., Christ University, Pune, School of Business & Management, Maharashtra, Pune, 412112, India; Aarif M., CAG Study Center, UP, Noida, India; Gangadharan S., St. Joseph's College of Engineering, Department of Management Studies, Chennai, India; Naqvi S.R., Amity University, Department of Human Resource and Organizational Behaviour, Amity Business School, Up, Noida, India; Assihalaf N.K., National University of Science and Technology, DhiQar, Iraq; Salih Mahdi A., AL-Nisour University College, Department of Medical Laboratories Technology, Baghdad, Iraq
A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
captioning; Digital data; disabled individuals; machine learning; media augmentation; similarity score; tags; vector; words
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE.
Andi H.K.; Senbagam S.; Sharma D.K.; Shelke M.; Kumar N.K.S.; Poonia R.C.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 438-443.
https://doi.org/10.1109/ICCSAI59793.2023.10420881 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186639436&doi=10.1109%2fICCSAI59793.2023.10420881&partnerID=40&md5=fa550daf75dc62eee3252365a758b0fc
Andi H.K., Asia Metropolitan University, Centre for Postgraduate Studies, Malaysia; Senbagam S., Dhanalakshmi Srinivasan College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nandu, Mamallapuram, India; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Shelke M., Aissms Ioit, Department of Artificial Intelligence and Data Science, Pune, India; Kumar N.K.S., School of Computing, Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology (Deemed to Be University), Department of Computer Science and Engineering, Tamil Nadu, Chennai, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India
Hyperledger Fabric as a Secure Blockchain Solution for Healthcare 4.0 Framework
Blockchain; Distributed Ledger Technology; Hyperledger Fabric platform; Zero-knowledge proof
The healthcare sector deals with extremely sensitive information that must be administered in a safe and confidential way. The objective of the proposed framework is to utilize Blockchain Technology (BT) for tracking medical prescriptions and the implementation is carried out using the Hyperledger Fabric platform, an enterprise-grade open-source distributed ledger technology platform designed for Bigdata applications. Multiple entities, including patients, e-pharmacies, pharmacies, doctors and hospitals can establish connections by introducing several nodes in the Fabric chain. A web-centered application is provided for doctors, connecting them with participating pharmacies, hospitals and e-pharmacies through which, they can share patient prescription. Pharmacies and e-pharmacies have access to this data and can notify patients about the availability of prescribed medicines. Additionally, reminders for refills, such as heart medication, can be sent for patients requiring long-term medication. Patients can also try with nearby pharmacies and the availability of their prescribed medicines. The inclusion of a wallet feature in the application enables patients to use mobile tokens for making purchases. Patient data is treated with the utmost confidentiality, kept private, and accessed only upon request and with the consent of the concerned parties. This privacy is ensured through the use of zero-knowledge proof. Patients retain access to their complete medical history, facilitating interactions with doctors without the need for repetitive information sharing. 2023 IEEE.
Joy S.; Baby Chithra R.; Neethu P.; Anju M.I.; Patil K.A.; Pal Cheema A.; Lal M.K.
2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 530-536.
https://doi.org/10.1109/ICAICCIT60255.2023.10466007 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189898930&doi=10.1109%2fICAICCIT60255.2023.10466007&partnerID=40&md5=11880ef854ba83868cff3f0e1633b0bc
Joy S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, 560103, India; Baby Chithra R., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, 560103, India; Neethu P., School of Engineering and Technology(SoET), Christ(Deemed to Be University), Department of Ece, Bangalore, India; Anju M.I., Faculty of Engineering and Technology, Srm Institute of Science and Technology, Dept of Ece, India; Patil K.A., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, 560103, India; Pal Cheema A., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, 560103, India; Lal M.K., Amrita School of Engineering, Dept of Ece, Karnataka, Bangalore, India
Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge Devices
Diabetic Retinopathy; Edge Computing; IoT (Internet of Things); Machine Learning; Remote Healthcare
This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. 2023 IEEE.
Deshmukh R.; Shalini S.; Vani V.D.; Kn N.M.; Alzubaidi L.H.; Raj V.H.
International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023
Deshmukh R., School of Engineering, Sr University, Telangana, Warangal, India; Shalini S., J.N.N Institute of Engineering, Department of Mathematics, India; Vani V.D., Institute of Aeronautical Engineering, Hyderabad, Dundigal, India; Kn N.M., Christ (Deemed to Be University), Department of Civil Engineering, Karnataka, Bengalore, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Raj V.H., New Horizon College of Engineering, Department of Applied Sciences, Bangalore, India
Artificial Intelligence, Smart Contracts, and the Groundbreaking Potential of Blockchain technology: Unlock the Next Generation of Innovation
Blockchain; Cloud computing authentication; P2P networks; Security; Smart Contracts
The blockchain technology consists of blocks and is a decentralized network of nodes (miners). Each block is made up of three parts: the data, the hash, and the hash from the previous block. After data has been stored, it is extremely difficult to temper the data. Transactions are verified by miners, who are compensated with a commission for their labor. Readers will gain a comprehensive understanding of blockchain technology from this review article, including how it may be used in a variety of industries including supply chains, healthcare, and banking. Most individuals were already familiar with Bitcoin as one of the well-known blockchain applications. In this section, we'll discuss a few of the countless research publications on the cutting-edge applications of this technology. We'll talk about the challenges that come with actually using these applications as well. Blockchain is an industry that is growing thanks to its more recent applications in a number of fields, such as hospital administration, cryptocurrency use, and other places. Only the manner that blockchain works and runs makes it possible for these applications. 2023 IEEE.
Upreti K.; Haque M.; Vats P.; Kumar V.V.; Divakaran P.; Tiwari A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 127-133.
Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Haque M., Pune (Deemed to Be University), Dr. D. y Patil Vidyapeeth's Centre for Online Learning, India; Vats P., Scse Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Kumar V.V., Malla Reddy Engineering College for Women Autonomous Mysammaguda, Via Kompally, Telangana, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Tiwari A., School of Business Studies, Christ (Deemed to Be University) Delhi Ncr, Ghaziabad, India
SARIMA Techniques for Predictive Resource Provisioning in Cloud Environments
Cloud Environments; Predictive; Provisioning; Resource; SARIMA; Time Series Forecasting
Seasonal Autoregressive Integrated Moving Average (SARIMA) models for dynamic cloud resource provisioning are introduced and evaluated in this work. Various cloud-based apps provided historical data to train and evaluate SARIMA models. The SARIMA(1,1,1)(0,1,1)12 model has an MAE of 0.056 and an RMSE of 0.082, indicating excellent prediction ability. This model projected resource needs better than other SARIMA settings. Sample prediction vs. real study showed close congruence between projected and observed resource consumption. MAE improved with hyperparameter adjustment, according to sensitivity analysis. Moreover, SARIMA-based resource allocation improved CPU usage by 12.5%, RAM utilization by 20%, and storage utilization by 21.4%. These data demonstrate SARIMA's ability to forecast cloud resource needs. SARIMA-based resource management might change dynamic cloud resource management systems due to cost reductions and resource usage efficiency. This research helps industry practitioners improve cloud-based service performance and cost. 2023 IEEE.
Bhat C.R.; Prabha B.; Donald C.; Sah S.; Patil H.; Firos A.
2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023
https://doi.org/10.1109/ICCEBS58601.2023.10449163 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189156755&doi=10.1109%2fICCEBS58601.2023.10449163&partnerID=40&md5=8403e06d74a978af6cfacd4333cad04d
Bhat C.R., Saveetha School of Engineering (Saveetha Institute of Medical and Technical Sciences), Department of Computer Science and Engineering, Tamilnadu, Chennai, India; Prabha B., Loyola-ICAM College of Engineering and Technology, Department of It, Chennai, India; Donald C., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Sah S., School of Set, Sharda University, Greater Noida, India; Patil H., Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India; Firos A., Rajiv Gandhi University (A Central University)Rono-Hills, Department of Computer Science and Engineering, Arunachal Pradesh, Doimukh, India
Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning
Adversarial Attacks; Deep Learning; Defensive Approach; Machine Learning; Model Distillation
Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. 2023 IEEE.
Suresh K.; Radha J.; Thilagaraj T.; Subramani R.; Lakineni P.K.; Taqui S.N.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 921-925.
Suresh K., Christ (Deemed University), Department of Computer Science, Bangalore, India; Radha J., Nandha Engineering College, Department of Computer Science and Engineering, Tamilnadu, Perundurai, India; Thilagaraj T., Moodlakatte Institute of Technology, Department of Computer Science and Engineering, Moodalakatte, Karnataka, Kundapura, India; Subramani R., Christ (Deemed to Be University), Department of Mathematics, Bangalore, India; Lakineni P.K., Gitam School of Technology, Gitam University, Department of Computer Science and Engineering, Visakhapatnam, India; Taqui S.N., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Department of Vlsi Microelectronics, Tamil Nadu, Chennai, India
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
computer-aided detection; hybrid deep learning; Lung cancer; memory efficient
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE.
Prasanna K.R.; Kumar R.V.; Mohanbabu G.; Selvam J.J.D.; Kumarnath J.; Sellapandi S.P.
2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023
https://doi.org/10.1109/ICDSAAI59313.2023.10452545 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187793442&doi=10.1109%2fICDSAAI59313.2023.10452545&partnerID=40&md5=cfcd4da5f399314751eda2033b4028b0
Prasanna K.R., Bharath Institute of Higher Education and Research, Chennai, India; Kumar R.V., Sri Lakshmi Narayana Institute of Medical Science, Department of Physiology, Biher, India; Mohanbabu G., Chennai Institute of Technology, Department of Ece, Chennai, India; Selvam J.J.D., Christ(Deemed to Be University), School of Business and Management, Banglore, India; Kumarnath J., Psna College of Engineering and Technology, Dindigul, India; Sellapandi S.P., Psna College of Engineering and Technology, Dindigul, India
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Deep learning; Efficientnet; Inceptionv3; Visual Geometry Group(VGG)
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE.
Kamalam G.K.; Rajasekar V.; Krishnasamy L.; Fathima Kadhoon M.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Kamalam G.K., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India; Rajasekar V., Kongu Engineering College, Department of Computer Science and Engineering, Tamil Nadu, India; Krishnasamy L., School of Engineering and Technology, Christ University, Department of Cse, Bengaluru, India; Fathima Kadhoon M., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
Natural Language Processing; NLTK; Polarity; Python; Sentiment Analysis; Subjectivity; TextBlob; Tokenization
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE.
Nehal; Jeet D.; Sharma V.; Mishra S.; Iwendi C.; Osamor J.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
Nehal, Kalinga Institute of Industrial Technology, India; Jeet D., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mishra S., Kalinga Institute of Industrial Technology, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, United Kingdom
KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
Algorithm; Semantic Heterogeneity; Semantic Web; Statistical Techniques; Web 3.0
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE.
Ranjan H.; Mishra S.; Al-Khasawneh M.A.; Singhal M.; Sharma V.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 669-674.
https://doi.org/10.1109/UPCON59197.2023.10434773 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187316697&doi=10.1109%2fUPCON59197.2023.10434773&partnerID=40&md5=bb9e587ae014d07aa1c6da63cd74dfec
Ranjan H., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Al-Khasawneh M.A., School of Computing, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Singhal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Department of Computational Sciences Christ (Deemed to Be University), Delhi, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Time Series Forecasting of Stock Market Volatility Using LSTM Networks
Deep learning; Financial modeling; GARCH models; LSTM networks; Risk management; Stock market; Time series; Trading strategies; Volatility forecasting
Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting. 2023 IEEE.
https://doi.org/10.1109/ICCAKM58659.2023.10449596 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187554066&doi=10.1109%2fICCAKM58659.2023.10449596&partnerID=40&md5=1aacde6e1e659aa4c01f9f3c66386d3d
Praveenraj D.D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Pandey M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Victor M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
anchor; explainability; lime; ml interpretability; shap; xai
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE.
Sujana D.S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Augustine D.P., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India
Automation using Artificial Intelligence in Business Landscape
Artificial Intelligence; Automation; Companies; Machine learning; Management; Technologies
The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks. 2023 IEEE.
Unni M.V.; Rudresh S.; Bh R.; Krishnan R.K.; Kar R.; Devichandrika S.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 1519-1523.
https://doi.org/10.1109/ICACRS58579.2023.10404252 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185384220&doi=10.1109%2fICACRS58579.2023.10404252&partnerID=40&md5=fc3a0fcb3937cc35220fd3b9ba8145e9
Unni M.V., Department of Management, St. Claret College, Karnataka, Bengaluru, India; Rudresh S., Department of Management, St. Claret College, Karnataka, Bengaluru, India; Bh R., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Krishnan R.K., College of Economics and Business Administration, University of Technology and Applied Sciences, Shinas, Oman; Kar R., Department of Management, St. Claret College, Karnataka, Bengaluru, India; Devichandrika S., Department of Business Administration, St. Francis De Sales College, Karnataka, Bangalore, India
A Comprehensive Study of Blockchain Technology Based Decentralised Ledger Implementations
Blockchain; blockchain applications; P2P network; Smart contracts; Smart healthcare
Information management and decentralized, secure transactions are made possible by the groundbreaking idea of blockchain technology. Blockchain has attracted considerable attention from a wide range of businesses as a result of the rising popularity of cryptocurrencies like Bitcoin and Ethereum. The primary goal of this paper is to present a thorough examination of decentralized ledger systems based on blockchain technology. It examines at the basic ideas, underpinnings, and real-world uses of blockchain in many industries. The ability to scale, effectiveness, privacy, security, seamless integration, governing scenarios, and consumption of energy are just a few of the technical factors that are looked at in relation to the deployment of blockchain technology. In this research paper we also pinpoint forthcoming developments and future prospects for the blockchain industry like Solutions for scalability at the subsequent layer, protocols for seamless integration, integration with the Internet of Things (IoT), apps for decentralized finance (DeFi), and digital currencies that are issued by central banks (CBDCs) are a few examples. 2023 IEEE.
Haque M.; Upreti K.; Ohol A.; Pisolkar G.; Vats P.; Kumar N.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 134-140.
Haque M., D.Y Patil Vidyapeeth's Centre for Online Learning Pune (Deemed to Be University), Pune, India; Upreti K., Dept. of Computer Science CHRIST(Deemed to Be University) Delhi Ncr, Ghaziabad, India; Ohol A., Faculty of Management Savitribai Phule Pune University, Pune, India; Pisolkar G., Faculty of Management St. Joseph's Degree & Pg College, Hyderabad, India; Vats P., Dept. of Cse, Scse, Manipal University Jaipur, Rajasthan, Jaipur, India; Kumar N., School of Business and Management CHRIST(Deemed to Be University), Bangalore, India
EEG Signals Acquisition and Processing of Mental Tasks for Controlling Smart Systems
ADT24 LabChart software; Brain Computer Interface; Electroencephalogram; Human Computer Interface; Man Machine Interface; Motor Neuron Diseases; Spinal cord Injury
With the help of this biosignal, we can create various kinds of interfaces. These interfaces were especially used for paralyzed individuals who have problems in the normal bio-sensor channels. Electroencephalogram (EEG) signals play a vital role in semi-paralyzed and fully paralyzed individuals to make communication with their caretakers. EEG signals are used not only for paralyzed individuals, nowadays most of home appliances are designed due to their reliability and accuracy. EEG based Smart devices are very easy to operate because signals are generated by the human brain automatically whenever the task is performed by humans. Through this research, we discussed the basic methodology needed for interfacing the EEG signals with smart devices. From this paper, most of the researchers will be going to know how to connect the EEG signals with the smart systems to control the external devices and induce the researchers to apply the EEG signals in real time. 2023 IEEE.
Sivasakthivel R.; Rajagopal M.; Ramar G.; Sivaraman G.
Sivasakthivel R., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Rajagopal M., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Ramar G., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Sivaraman G., M.G.R.College, Tamilnadu, Hosur, India
Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
Benign; Cervical Cancer; Logistic Regression; Malignant; ML; Random Forest
One of the incurable diseases that affect women is cervical cancer. It is brought on by a protracted infection of the skin and the vaginal mucous membrane cells. The Human Papilloma Virus (HPV), is the main factor causing aberrant cell proliferation in the area around the cervix. There are no symptoms present when the illness first appears. Early detection of this malignancy may be used to prevent death. People in less developed countries cannot afford to periodically examine themselves due to a lack of awareness, poor medical infrastructure, and expensive medication. The EDA technique is applied to examine the data and understand its characteristics. Machine Learning algorithm has been used to diagnose cervical cancer. In order to spot the existence of cervical cancer, five machine learning classifiers are utilized, the algorithms to begin earlier. The Logistic Regression classifier's results validate the correct stage prediction. 2023 IEEE.
Santhiya S.; Mapari S.; Abinaya N.; Jayadharshini P.; Priyanka S.; Krishnasamy L.
2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 582-587.
Santhiya S., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Mapari S., Symbiosis International (Deemed University), Symbiosis Institute of Computer Studies and Research (SICSR), Pune, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, TamilNadu, Erode, India; Krishnasamy L., Christ (Deemed to Be) University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India