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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

computers, Materials & Continua 2022年第6期71卷 4523-4554页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling 2

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, R.N. Pati, Peeta Basa Deepa, K. Konidhala, Jothibabu K Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering India Amrita School of Engineering Department of Electrical & Electronics Engineering Bangalore India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350397635

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same. © 2023 IEEE.

关键词： Multiple linear regression

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Enhancing Energy Efficiency in IoT Networks Through Fuzzy Clustering and Optimization

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Mobile Networks and Applications 2024年第5期29卷 1594-1617页

作者： Javadpour, Amir Sangaiah, Arun Kumar Zaviyeh, Hadi Ja’fari, Forough Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal International Graduate School Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Economics and Statistics Guangzhou University Guangzhou510006 China Department of Computer Engineering Sharif University of Technology Tehran Iran

Wireless Sensor and Internet of Things (WSIoT) networks are characterized by nodes scattered throughout the environment, posing a significant challenge when it comes to battery replacement. The task of developing an algorithm that effectively reduces energy consumption in IoT networks through the utilization of artificial intelligence and fuzzy logic is a formidable one. Heuristic algorithms emerge as valuable tools in this context, capable of swiftly addressing non-deterministic polynomial problems or approximating optimal solutions with remarkable accuracy. Conversely, mathematical optimization approaches often falter due to their sluggishness or inefficiency, rendering them less suited for the dynamic demands of IoT networks. This study embarks on a novel approach, leveraging the synergy of fuzzy clustering to intricately connect sensors to the network and particle optimization to approximate the initial values of head clusters within the realm of WSIoT. A comprehensive evaluation of our proposed strategy is meticulously conducted through extensive simulations utilizing the NS2 simulator. In this evaluation, we distinguish our algorithm from existing methods, including IoTbWSN and REcsIoT. The results of this comparative analysis are illuminating. In contrast to other methodologies, our proposed approach demonstrates a notable 9.57% improvement in network throughput and a substantial 8.47% reduction in energy consumption. This signifies a remarkable leap forward in the quest to address the challenges posed by energy consumption and battery replacement within the complex landscape of IoT networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Energy utilization

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Light trapping enhanced broadband photodetection and imaging based on MoSe_(2)/pyramid Si vdW heterojunction

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Nano Research 2023年第7期16卷 10552-10558页

作者： Shaoqin Pan Shuo-En Wu Jinjin Hei Zhiwen Zhou Longhui Zeng Yakun Xing Pei Lin Zhifeng Shi Yongtao Tian Xinjian Li Di Wu School of Physics and Microelectronics Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Two-dimensional(2D)layered materials have been considered promising candidates for next-generation ***,the performance of 2D photodetectors still has much room for improvement due to weak light absorption of planar 2D materials and lack of high-quality heterojunction preparation ***,2D materials integrating with mature bulk semiconductors are a promising pathway to overcome this limitation and promote the practical application on *** this work,we present the patterned assembly of MoSe_(2)/pyramid Si mixed-dimensional van der Waals(vdW)heterojunction arrays for broadband photodetection and *** from the light trapping effect induced enhanced optical absorption and high-quality vdW heterojunction,the photodetector demonstrates a wide spectral response range from 265 to 1550 nm,large responsivity up to 0.67 A·W^(-1),high specific detectivity of 1.84×10^(13)Jones,and ultrafast response time of 0.34/5.6μs at 0 ***,the photodetector array exhibits outstanding broadband image sensing *** study offers a novel development route for high-performance and broadband photodetector array by MoSe_(2)/pyramid Si mixed-dimensional heterojunction.

关键词： two-dimensional(2D)MoSe_(2)layers light trapping van der Waals(vdW)heterojunction broadband photodetector selfpowered imaging

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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

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SN computer Science 2023年第6期4卷 710页

作者： Patil, Manoj A. Manohar, M. Department of Computer Science and Engineering Christ (Deemed to be University) School of Engineering and Technology Kengeri Karnataka Bengaluru 560074 India Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad 500104 India

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Hybrid classifier LSTM SISS-OB

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A Comprehensive Review Analysis of Distinct Tools Image Plagiarism Detection

A Comprehensive Review Analysis of Distinct Tools Image Plag...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Naudiyal, Anjali Singh, Vineeta Joshi, Kapil Roka, Sanjay Mamoria, Pushpa Shakya, Rajeev Kumar School of Applied and Life Sciences Uttaranchal University Premnagar Dehradun India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Uttaranchal University Department of Computer Science & Engineering Dehradun India Graphic Era deemed to be university dehradun India School of Electrical Engineering and Computing Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia

ISBN: (纸本)9798350379716

In the era of artificial intelligence, academic activities tend to use Chat GPT more frequently. A Chabot called Chat GPT is used for brainstorming, writing, and learning. Although most academicians use Chat GPT to their advantage when taking notes, it is detrimental to research. A few authors wrote the papers swiftly by using AI techniques. These days, chat GPT is employed in a wide range of tasks. It prepares any type of content for the internet. The likelihood of plagiarism is rising as a result of its use. At present, plagiarism is the biggest challenge in the field of research. Plagiarism occurs when an author uses this technology to modify another person's work and transmit it off as their own without providing a reference or credit. Plagiarism is a term used to describe when someone copies something from another source without providing credit to the original author. Google is working quickly in the image field, but at that point, it will be used to detect plagiarism that is generated with the aid of artificial intelligence. The less papers that are utilized in favor of online resources, the more academics are working to make the globe more environmentally friendly. In the presented paper, a few software applications are discussed and compare a number of different techniques to detect the plagiarism in academics field like scientific equations or cross-lingual or multilingual texts. Various applications are developed in research to identify instances of plagiarism in research papers, assignments, and source code. After reviewing the papers pertaining to various tools for detecting plagiarism, not much more work is done in the area of detecting plagiarism in images or in image text. The article that is being presented comes to the conclusion that no software tool has been developed to identify plagiarism in images or in the text associated with images. © 2024 IEEE.

关键词： Application programs

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. Based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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AI Based Cloud Failure Detection and Prevention Algorithm

AI Based Cloud Failure Detection and Prevention Algorithm

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Moni, Md Moynul Ashik Niloy, Maharshi Fahmid-Ul-Alam Juboraj, Md Banik, Arnob Department of Computer Science and Engineering BRAC University Dhaka Bangladesh School of Computer Science and Engineering VIT University Tamil Nadu Vellore India

ISBN: (纸本)9798350325188

It is crucial to anticipate and avoid cloud breakdowns to guarantee the availability and dependability of cloud services. Machine learning algorithms have shown promise in forecasting and preventing cloud failures by analyzing large datasets of cloud usage traces to identify potential failure points. This paper presents an algorithm incorporating different Machine Learning and Deep Learning models for cloud failure prediction and prevention based on analyzing the Google cluster usage traces dataset. The proposed algorithm, the AI-based Cloud Failure Prevention Algorithm (ACFP), utilizes various features to predict potential failure points. The proposed algorithm used various AI models, the prominent ones are Cat Boost (accuracy, F1 score, Precision, Specificity: 100%), LGB (accuracy, F1 score, Precision, Specificity: 100%), Gradient Boost (accuracy, F1 score, Precision, Specificity: 100%), Random Forest (accuracy, F1 score, Precision, Specificity: 100%), etc. for the precise detection of cloud task *** that the ACFP selects one ML/DL model with best failure prediction efficiency and use some parameters like:"resource_utilization","event_type", and"scheduler" to generate some suggestion based values for the cloud so that the cloud implements that suggestion to prevent cloud failure. The experimental results show how well the algorithm predicts and averts cloud breakdowns, highlighting its potential to raise the reliability and availability of cloud services. The comparison of the failure rate of the cloud by predicting job failures with and without the ACFP is 2.67% and 22.76%, respectively. This algorithm can raise the quality of cloud services and reduce the risks of cloud failures. © 2023 IEEE.

关键词： Artificial intelligence Cloud Algorithms Cloud computing Failure prediction Machine learning

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A Dynamic Heuristic Set-based Particle Swarm Optimizer for the Multi-Objective Task Assigning Problem in Wireless Sensor Networks 11

A Dynamic Heuristic Set-based Particle Swarm Optimizer for t...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Wei, Feng-Feng Geng, Ming-Can Hu, Xiao-Min Jeon, Sang-Woon Chen, Wei-Neng South China University of Technology School of Computer Science and Engineering Guangzhou China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350385670

The task assigning problem in wireless sensor networks is a challenging NP-hard combinatorial optimization problem that has caught much attention. Although binary particle swarm optimizers have been proven to be effective for this problem, the prematurity remains severe. To fix the prematurity, this paper proposes a set-based particle swarm optimizer with dynamic heuristic substitution techniques. Differing from the original binary particle swarm optimizer, the representation scheme is based on the concept of set. The position of each particle is a set whose elements are defined by the problem. A particle position element denotes the allocation of a task to a sensor node. The proposed algorithm tries to select an optimal subset from the universal set based on the particle swarm optimization framework. Moreover, the comprehensive learning strategy that utilizes beneficial information from the best-ever positions of all particles is employed, which helps keep the swarm away from local optima. Considering the constraints in real-world situations, the position updating adopts a substitution operation, guided by heuristic information that dynamically alters. To improve particle diversity, mutation operations are carried out after renewing the particle positions. Simulation results demonstrate the superiority of the proposed approach over others. © 2024 IEEE.

关键词： Combinatorial optimization

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