Journal of Advanced Applied Scientific Research (JOAASR)
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Pedestrian detection algorithm based on improved YOLOv3
Get PDFThe ability to accurately detect pedestrians in the area of interest in real time is crucial in the field of autonomous driving. An improved YOLOv3 model is proposed for pedestrian detection. Firstly, a lightweight model that incorporates a residual network module approach and a CBAM attention mechanism is added to the structure to enhance the feature representation capability of the network. Experimental results show that the improved YOLOv3 target detection model raises the detection accuracy by 4% compared to the original algorithm, and the accuracy precision is improved to a large extent, which verifies the feasibility and effectiveness of the improved YOLOv3 model for pedestrian detection
Synthesis of silver oxide nanoparticles using aqueous leaf extracts of Viscum orientale Willd, Coleus amboinicus Lour and evaluation of their antibacterial activity
No full textThe objective of this study was to develop a straightforward biological synthesis of silver oxide nanoparticles (Ag2O NPs) using aqueous leaf extract of Viscum orientale Willd and Coleus amboinicus Lour as a good lowering and capping agent . The UV–visible bands observed at 499 and 451 nm was confirmed the creation of nanosized Ag2O particles making use of Viscum orientale Willd and Coleus amboinicus Lour plants. The formation of Ag2O stretching frequency was confirmed by FTIR spectral studies. Scanning electron microscopy and EDAX analysis displayed Ag2O nanoparticles have been pure and spherical shaped and had been the variety of length from 35±2 nm for (Ag2O NPs) by the usage of Viscum orientale Willd and 32±2 nm for (Ag2O NPs) Coleus amboinicus Lour vice-versa. The fashioned nanoparticles have been cubic in association and face-centered cubic in form, consistent with X-ray diffraction studies. The shaped green kind Ag2O NPs confirmed ambitious antibacterial against each gram possitive and gram negative bacteria
Enhancing Diagnostic Accuracy with a Novel Computer Application for Quantitative Analysis of Bio-Medical Infrared Thermal Images
Get PDFTemperature is a parameter that acts as a valuable indicator for understanding persisting disorders and illnesses in the human body. Body surface temperature is measured through the skin and the body’s internal temperature is measured through the mouth or rectum, which are used as vital information reflecting the state of thermo-regulation, a sub-process of the body's homeostasis, which is required for its normal functioning. In a state of functional imbalance, the affected region emits thermal radiation that is above or below the normal range. Thermal imaging of body regions is a beneficial means of detection of such thermal imbalances and the temperature data of each image can be analyzed quantitatively to be able to correlate the results clinically. In this article, a computer-based GUI – MedTherm Image Viewer and Analysis Tool developed in MATLAB is proposed for the processing and quantitative evaluation of thermal images for the purpose of providing supportive aid to the existing medical diagnostic procedures. The suggested graphical user interface (GUI) is beneficial in computing statistical features based on histograms of thermal images that have been recognized in numerous other studies as valuable parameters that assist in clinical diagnostic procedures
Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models
Get PDFRecently, honey has become a target of falsification using inexpensive artificial sugar syrup. Current methods for detecting honey adulteration are destructive, slow, and expensive. This paper aims to use hyperspectral imaging (HSI) coupled with Machine Learning (ML) techniques to predict and quantify honey adulteration. The honey adulteration prediction approach proposed in this paper comprises two main steps: spatial and spectral dimensionality reduction and adulteration prediction. We used mathematical averaging to reduce spatial features and employed the Principal Component Analysis and Linear Discriminant Analysis algorithms for spectral feature extraction. Five ML regression models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and K-Nearest Neighbor Regression (KNNR), were used for predicting the sugar concentration in honey. We used a public honey HSI dataset to assess the proposed system's performance. Results show that KNNR outperformed other models in quantifying honey adulteration, achieving a coefficient of determination R2 of 0.94 and a Root Mean Squared Error (RMSE) of 5.12. Findings indicate that HSI coupled with ML models can provide a fast and nondestructive prediction of honey adulteration
Emotional Speech Recognition for Marathi Language
Get PDFA spontaneous mental state, emotion does not result from deliberate effort. There are many different kinds of emotions in a speech. Because it enhances interactions between people and technology, automatic emotion identification from human speech is becoming more common today. Several temporal and spectral features of human speech can be extracted. Several methods can be used to categorise pitch-related traits, Mel Frequency Cepstral Coefficients (MFCCs), and speech formants. This study looks at statistical characteristics, including MFCCs and linear discriminant analysis, which were used to categorise these properties (LDA). This article also describes a database of artificially emotionalized Marathi speech. The data samples were collected from Marathi speeches given by men and women that mimicked the emotions that resulted to Marathi utterances that could be utilised in everyday conversation and are interpreted in all analysed emotions. To identify data samples, three essential categories—happy, sad, and angry—were used. For MFCC and LPC, the training accuracy and testing accuracy are 98, 82 and 85,82 respectively
A Machine Learning Approach to Enhance Semantic Understanding in Knowledge Engineering
Get PDFDeveloping complex systems in environments of various domains need effective way to share, capture, and integrate knowledge of experts. “Modern Knowledge Engineering (KE)” systems meet this function to execute dignified knowledge with highly dedicated languages and environments. However, commitment of such environments to their application domain poses restrictions on incorporation of KE across the domain. Using Semantic Understanding (SU) can deliver a domain-neutral option to formalize knowledge and integrate data to reduce the effort needed for integration of knowledge of various domains in one representation.
This paper discusses machine learning approaches used to solve problems related to knowledge engineering. Semantic Understanding has seen a lot of improvements over the decades as per industrial demands and human needs. This new era is related to teaching machine to learn itself and understand the purpose and concept of its use with algorithms. This paper discusses semantic technology used in machine learning and its idea. It briefly discusses the important role of “machine learning and semantic technology”
Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling
Get PDFCloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service
Emotional Intelligence in Text-To-Speech Synthesis in Pali Language Using Fuzzy Logic
Get PDFThe field of emotional text-to-speech (TTS) synthesis is making swift progress within the realm of artificial intelligence, holding immense promise to transform our interaction with technology. By using advanced algorithms to analyze and understand the emotional content of text, these systems are able to produce spoken language that accurately conveys the intended emotional tone of the message. Despite the existence of several Text-To-Speech systems across various languages, Pali language is yet to have its own. As a result, we have taken the initiative to create a Text-To-Speech synthesizer exclusively for Pali. Our system offers an end-to-end solution for emotional speech synthesis via Text-To-Speech. We address the problem by incorporating disentangled, well-grained prosody features with global, sentence-level emotion implanting. These well-grained features learn to denote local prosodic differences disentangled from the speaker, tone, and worldwide emotion label. Prosody is usually modeled by rules, so we have implemented the fuzzy logic system to develop a controller for the prosody of Pali speech. The fuzzy controller handles different linguistic parameters in three types of sentences: interrogative, exclamatory, and declarative. The final system produces comprehensible speech that mimics the appropriate intonation for every type of sentence.
In this paper, we introduce and outline the application of a fuzzy paradigm to incorporate a Text-To-Speech system for the Pali language while preserving a rule-based Concatenative synthesizer. In the outline of classic Concatenative TTS systems, we recommend a new method in order to increase Concatenative unit selection computation, directed at increasing synthetic speech perceptual superiority. In order to tackle the problem of phonemes that are prone to multiple descriptions in rule-based speech synthesis, the proposed solution involves a fuzzy system.
In the introductory section, we offer a concise description of the current context surrounding the challenge of emotional speech synthesis. The second section of this paper outlines the notable advancements made in emotional speech synthesis, acknowledging the contributions of various researchers in this field. The third section delves into the technical details of implementing a fuzzy system The last section of the paper presents the main conclusions and future research scope
Retraction notice to "Indoors Fitness Training Monitoring based on OpenPose" Vol. 6 No. 3 (2024):Journal of Advanced Applied Scientific Research-ICKE-2023
Get PDFTitle of the Article: "Indoors Fitness Training Monitoring based on OpenPose"Author(s): J.Haoran, S. Karungaru, & K. Terada
DOI: https://doi.org/10.46947/joaasr632024947
We regret to announce the retraction of the article titled "Indoors Fitness Training Monitoring based on OpenPose" by J.Haoran, S. Karungaru, & K. Terada, which was originally published in volume Vol. 6 No. 3 (2024): JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH-ICKE-2023 on 30-05-2024.
This retraction follows several unresolved issues that arose after publication:
Failure to Provide Valid Justification for Reference Changes: The author(s) requested significant changes to the references in the article but failed to provide the necessary and valid justification for these changes in the required format. Despite multiple requests for clarification, the justification provided was insufficient. Given the importance of proper referencing for the accuracy and credibility of academic work, this issue raised serious concerns.
Image Duplication Detected: During the post-publication review, it was discovered that some images in the article had been duplicated from previously published papers. This raised significant concerns regarding the integrity and originality of the data presented in the publication.
The authors had previously requested retraction of the article if their proposed reference changes were not accepted. Given this request and the issues identified, we have decided to proceed with the retraction of the article.
We sincerely apologize to our readers for any inconvenience caused by this retraction. Our commitment to maintaining the highest standards of scholarly publication remains steadfast, and we will continue to ensure that all content published in JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH adheres to these rigorous standards
A Multilayer Data Security Using Matrix Transformation and RSA For Public Cloud Storage
Get PDFCloud computing is an internet-based system that allows clients to access computers, software, infrastructure, devices, and other resources through a subscription model. Data security is a major concern for the cloud computing model. Data security is a research challenge in any cloud environment. When cloud users upload their confidential and secret data through the cloud, the security of this data must be ensured. In order to improve data security, a new method is presented in this paper. The approach uses ten random prime integers to compute the public and safe private keys after transforming user data into a matrix format. The encrypted content is then decrypted using the secured private key, improving total data security. The Hackman tool is used to analyse the encryption and decryption times as well as the security level. The OPNET tool measures the power of encryption and decryption. The findings indicate that all five potential file sizes for the proposed algorithm's parameters have the highest value. The cypher text was analysed using this suggested technique using brute force and dictionary attacks. This method is more effective, more secure, and impenetrable