Journal of Integrated Science and Technology
https://pubs.thesciencein.org/journal/index.php/jist
<p>The Journal of Integrated Science and Technology is a peer reviewed journal for publication of fundamental and applied research from disciplines of science (Chemistry, Physics, Biology, Mathematics, environmental science, Interdisciplinary science) and technology (engineering fields and technology advances).</p>ScienceIn PublishingenJournal of Integrated Science and Technology2321-4635<p>Rights and Permission</p>A critical analysis of crop management using Machine Learning towards smart and precise farming
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a809
<p>Agriculture is one of the key industries that use ground-based and aerial drones for crop health evaluation, crop monitoring, crop spraying, planting, soil and field analysis, irrigation, and other fields. Drones can be flown from a ground station or from the air. The term "precision farming," commonly referred to as site-specific crop management, is the use of technology to increase agricultural output and efficiency. Due to the availability of real-time data and insights on crop growth, soil quality, weather patterns, and other crucial elements, the integration of machine learning (ML) and the internet of things (IoT) has completely changed the way farming is done. To put it another way, both plants and animals receive the exact care that they require, which is decided by machines with a precision that exceeds that of a human. Instead of making decisions for an entire field, precision farming enables decisions to be made on a per-square-meter or even per-plant or per-animal basis. This is the primary distinction between traditional farming and precision farming. This article focuses on the creation and application of a hybrid IoT and ML system for precise farming. The ML algorithms can process enormous amounts of data and produce insights that can assist farmers in making defensible decisions regarding their farming methods. The framework's IoT devices are in charge of gathering data from diverse sources and transmitting it to a central system for processing. Due to the hybrid nature of the framework, several technologies can be combined to produce a cohesive and effective system for precise farming.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.809</em></p>
Computer Sciences and MathematicsMachine LearningInternet of Things (IoT)Smart agriculturePrecise FarmingCrop SelectionRavi Ray ChaudharyKalyan Devappa BamaneHimanshi AgrawalP. MalathiAarti S. GaikwadAbhijit Janardan Patankar
Copyright (c) 2024 Ravi Ray Chaudhary, Kalyan Devappa Bamane, Himanshi Agrawal, P. Malathi, Aarti S. Gaikwad, Abhijit Janardan Patankar
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-102024-02-1080980910.62110/sciencein.jist.2024.v12.809In-silico molecular studies of the phytochemicals in ethanolic extract of Chromolaena Odorata against H+/K+-ATPase enzyme for Proton Pump inhibitor
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a801
<p><em>Chromolaenaodorata</em> popularly called devil weed (independent leaves) is a subtropical flowering shrub in the family of Asteraceae that has been used for the herbal treatment of wounds, burns, skin infections, and relieving painful stomach ulcers. There are some scientific literature reporting its antimicrobial, wound healing, hemostatic, antioxidant, anti-inflammatory, platelet protective, anticancer, hypoglycemic, hypolipidemic, insecticidal, and anti-anemic properties. This study investigated the phytochemical components and the anti-ulcer (gastric pump inhibition) properties of the ethanolic extract of the plant using GC-MS and in silico molecular docking. The GC-MS results from this study detected thirty (30) retentions with fifty-one (51) library/ID-suggested compounds. The docking of the detected compounds against gastric proton pump for the treatment of ulcer revealed that among the ligands that were docked with the enzyme (H<sup>+</sup>/K<sup>+</sup>-ATPase); (3-(Azepan-1-yl)-1,2-benzothiazole 1,1-dioxide) had better binding energy value (high binding energy value) (-8.4kcal/mol) compared to the standard anti-ulcer drug (omeprazole; −8.0 kcal/mol). The strong bonding of 3-(Azepan-1-yl)-1,2-benzothiazole-1,1-dioxide to the receptor suggests that the compound may possess better gastric proton pump inhibitory potential than omeprazole. This result may also validate the traditional use of the plant for gastric ulcer-relieving activity.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.801</em></p>
Biomedical and Pharmaceutical SciencesOmeprazolegastric pumpulcerIn-silico studyPhytochemical ConstituentsMolecular ModelingChemistryChinyere B.C. IkpaOluwatosin Maduka Tochukwu
Copyright (c) 2024 Chinyere B.C. Ikpa, Oluwatosin Maduka Tochukwu
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-012024-02-0180180110.62110/sciencein.jist.2024.v12.801Modelling EEG Signal using multivariate denoising and design of the optimized CSP filter for efficient feature extraction applicable in non-invasive BCI
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a814
<p>Electroencephalographic (EEG) signals corresponding to motor imagery (MI) are efficient input to the brain-computer interface (BCI), but are highly contaminated and have a spatial distribution of the activity related variation. This work proposes a filtering method having the combined advantage of wavelet transform and principal component analysis (PCA) for pre-processing of the signal. Transform domain helps to capture the main features of the signals using matching wavelet function while PCA reduces the feature dimension. Correlation structure of noise used here helps in cancelling interferences and to rebuild the signal. Optimized and subject-specific common spatial pattern (CSP) filter design is proposed for extracting the features. Empirical analysis of number of electrodes for building the CSP filter mask leads to selection of 21 electrodes from MI region gives the best performance. The method executes weighing of the electrodes and accordingly assigning the importance to the electrode while forming the filter. Filter induced optimized variance of the signals acts as the features for two-class support vector machine (SVM). Classification accuracy (CA) obtained for subject aa is 90.3%, and for subject al it is 99.2%. Subject aw having small training set gives accuracy to be 96.7% whereas for subject ay it is 96.3%.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.814</em></p>
EngineeringMultivariateCommon Spatial Pattern (CSP)Brain Computer Interface(BCI)Electroencephalogram (EEG)Vrushali G. RautSandhya A. ShirsatRohita P. PatilOmkrakash S. RajankarSupriya O. Rajankar
Copyright (c) 2024 Vrushali G. Raut, Sandhya A. Shirsat, Rohita P. Patil, Omkrakash S. Rajankar, Supriya O. Rajankar
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-152024-02-1581481410.62110/sciencein.jist.2024.v12.814Estimation of uncertainty in Brain Tumor segmentation using modified multistage 3D-UNet on multimodal MRI images
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a802
<p>Automated brain tumor segmentation is challenging due to the tumor tissues' shape, size, and appearance. Various methods used multi-mode MRI scans to segment sub-regions of brain tumors. 3D CNN methods improved performance in recent years, but most methods do not use uncertainty information in segmentation. For reliability and understanding, model prediction is vital for clinical decisions. This work studies three models namely 3D-UNet, Modified 3D-UNet, and Modified Multistage-3D-UNet for brain tumor segmentation. MRI volume bias correction and normalization were carried out using z-score normalization. Two patch generation strategies reduce memory use and class imbalance. Voxel-wise uncertainty evaluation was made for aleatoric and epistemic uncertainties using test time augmentation and dropout, respectively. Variance and entropy are used to measure the uncertainty of the modified multistage-3D-Unet segmentation model from ground truth. Variance creates separate uncertainty maps for each tumor sub-regions, whereas, entropy provides only global information. Uncertainty is used to filter miss-segmented predictions and improve accuracy. Uncertainty awareness increases model accuracy with dice scores of 0.93, 0.91, and 0.83 for tumor sub-regions WT, TC, and ET respectively.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.802</em></p>
Biomedical and Pharmaceutical SciencesBrain Tumor SegmentationuncertaintyMRI Images3D UNetDeep LearningBhavesh ParmarMehul Parikh
Copyright (c) 2024 Bhavesh Parmar, Mehul Parikh
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-022024-02-0280280210.62110/sciencein.jist.2024.v12.802Discriminative Autoencoder architecture for Acoustic Signal enhancement
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a812
<p>The speech signal is an important acoustic signal. The quality of speech signal is dependent upon the surroundings of the speaker and listener of speech, sound and audio. The additive noises such as white noise and babble noise severely degrade the performance of the sound-based applications. The conventional methods for noise reduction introduce musical noises in the enhanced speech signal. The discriminative networks map the noisy speech to the clean target speech signal. In this process, the discriminative networks add unpleasant distortions to the signal. Hence, two auto encoder based discriminative approaches: Discriminative UNET model (DUNET) and Discriminative De-noising Auto encoder model (DDAE) are designed and tested with noisy speech samples available from NOIZEUS dataset. The performance of the method is compared with four baseline methods: UNET, Variational Auto encoder, Convolutional auto encoder and Pixel CNN architecture. Five evaluation indexes, PESQ, STOI, SDR, improvement in SNR, and Segmental SNR are used for the comparison of performance. The architecture provides better intelligibility and less signal distortion ratio as compared to given baseline methods.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.812</em></p>
EngineeringDiscriminative networkAcoustic SignalAuto encoderSpeech De-noisingUNETShibani KarVishwajeet Mukherjee
Copyright (c) 2024 Shibani Kar, Vishwajeet Mukherjee
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2024-02-142024-02-1481281210.62110/sciencein.jist.2024.v12.812Assessment of the Ichthyofaunal Diversity in relation to Physico-chemical attributes of River Asan in the Garhwal Himalaya, India
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a807
<p>The present study investigated the water quality of River Asan based on various physicochemical parameters and its freshwater fish fauna in lower Himalaya. In two sampling sites 95 fish specimens were collected, 13 fish species belonging to 1 class, 4 orders, 4 families, 9 genera were recorded from the river Asan during the present study. <em>Barilius bendelisis </em>constituted high percentage in fish composition. The water quality of River Asan is good, and no substantial pollution was observed. Main 4 different ecological indices applied to fish data shows that the change in fish number is the seasonal phenomenon. However, decline in many fish populations observed, possibly due to illegal fishing and disturbance through anthropogenic activities. There is a need to protect their natural habitats, execute policies and motivate people for the management and conservation of fish varieties in this river.</p> <p>URN:NBN:sciencein.jist.2024.v12.807</p>
Environmental ScienceFish diversitywater qualityphysicochemicalPCARiver AsanAshu ChaudharyDhyal SinghDeepali RanaSarvesh RustagiShefalee SinghSneha SinghPallavi ChauhanNidhi Chatterjee
Copyright (c) 2024 Ashu Chaudhary, Dhyal Singh, Deepali Rana, Sarvesh Rustagi, Shefalee Singh, Sneha Singh, Pallavi Chauhan, Nidhi Chatterjee
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-092024-02-0980780710.62110/sciencein.jist.2024.v12.807DV-PSO-Net: A novel deep mutual learning model with Heuristic search using Particle Swarm optimization for Mango leaf disease detection
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a804
<p>Efficient identification of diseases in mango leaves is crucial for maintaining the health and productivity of mango crops. Existing approaches, relying on manual inspection or image processing, are time-consuming, error-prone, and face challenges with various image conditions. This study addresses these issues by proposing a robust machine learning model capable of classifying mango leaf diseases across diverse conditions, including different resolutions, structural complexities, and varying blur levels. The solution involves exploring and optimizing machine learning algorithms, tuning hyperparameters, and developing a predictive model for accurate disease identification based on visual features extracted from leaf images. To overcome these challenges, a novel deep mutual learning model, DVNet, is introduced, leveraging the strengths of Densenet 121 and VGG19 neural networks. Hyperparameter optimization, a systematic procedure for identifying optimal values, is incorporated using Particle Swarm Optimization (PSO). The proposed framework achieves an impressive accuracy of 94.72% in detecting eight distinct disease categories and healthy mango leaves, surpassing existing works in mango leaf disease detection.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.804</em></p>
Computer Sciences and MathematicsMango Leaf disease DetectionDenseNet-121VGG-19Particle Swarm optimizationC.P. VijayK Pushpalatha
Copyright (c) 2024 C.P. Vijay, K Pushpalatha
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-062024-02-0680480410.62110/sciencein.jist.2024.v12.804Navigating the frequency of Cervical Vertigo: Insights and statistics
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a797
<p>This study focuses on investigating the prevalence of cervical vertigo, delving into the existing knowledge about how frequently it occurs and its distribution across different populations. In ACS Medical College and Hospital, Abitha Modern Physio Center in Kolathur, Blossom Rehabilitation Clinic in Kolathur, and F.O.R Ortho and Neuro Specialty Hospital, a six-month cross-sectional observational study was carried out. The investigation was carried out from January 2023 to June 2023. 244 people in the age range of 40 to 60 made up the sample for the study. Utilizing the vertigo symptom scale, the prevalence was gathered. 244 participants categorized by age, gender, BMI to investigate the prevalence of dizziness. From the statistical data it is clearly stated that female subjects were more affected than men. 41 - 60 Years age group were identified more with complaint. The BMI Classification showed that more of the affected people were overweight. Percentage distribution of dizziness in the Overall Participants using vertigo symptom scale, much of the participants had mild and moderate dizziness. Prevalence of cervical vertigo on different populations were demonstrated in this study. This study sheds light on the prevalence of dizziness across different age groups, gender disparities, and its relationship with BMI classification. The findings contribute to a better understanding distribution of dizziness.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.797</em></p>
Biomedical and Pharmaceutical SciencesCervial VertigoDizzinessVertigo symptom scaleG. VaishnaviJibi PaulPrathap SuganthirababuC.V. Senthilnathan
Copyright (c) 2024 G. Vaishnavi, Jibi Paul, Prathap Suganthirababu, C.V. Senthilnathan
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-232024-01-2379779710.62110/sciencein.jist.2024.v12.797Frequent CNN based ensembling for MRI classification for Abnormal Brain Growth detection
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a785
<p>Digital image processing is a key player in the analysis of medical images, particularly in understanding the intricacies of abnormal brain growth development. Notably, the application of CNN algorithms to MRI images accelerates abnormal brain growth detection with enhanced accuracy; facilitating prompt decision-making by radiologists. This research focuses on finding abnormal brain growth using advanced CNN computer techniques. The study is split into three main steps. In the first step, brain MRI images are pre-processed by applying selected pre-processing techniques. In the second step, machine learning feature extraction methods are applied to pick out important features from these images. Finally, CNN models such as VGG, ResNet, DenseNet, and MobileNet are applied to classify the MRI images at a detailed level. The ensemble is done to improve the accuracy of the classification of MRI images. The results from study indicate easy automated abnormal brain growth detection that save radiologists' time and improve the efficiency of early diagnosis.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.785</em></p>
Computer Sciences and MathematicsConvolutional Neural NetworkEnsembleBrain TumourDigital PathologyCNNDigital Image ProcessingCancer ImagingMRI ImagesVipul V. BagMithun B. PatilSanika Nagnath Kendre
Copyright (c) 2023 Vipul V. Bag, Mithun B. Patil, Sanika Nagnath Kendre
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-272023-12-2778578510.62110/sciencein.jist.2024.v12.785Novel Fault tolerance in Three Stage Solid State Transformer
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a806
<p>Solid State Transformer (SST) have gained a considerable attention of designer and engineers. They offer many advantages over conventional transformer like voltage regulation, compact design, flexibility, high efficiency and power quality. In addition, the SST provides an intermediate DC bus connection between renewable energy sources and storage battery. This enables SST to de deployed in distribution and renewable energy system. In such systems a continuous operation is desired. The power electronics semiconducting switching devices raises a concern about the reliability of SST. A slight over current to the rated capacity of semiconducting devices can damage the SST permanently where a normal transformer's copper winding has capacity to withstand the short circuit current. In this paper, a fault tolerant three stage SST with redundancy is discussed. For fault identification voltage sensing method is deployed for the stages of dual active bridge converter (DC-DC converter) and inverter (DC-AC converter) stages and current sensing method for PWM rectifier (AC-DC converter) stage. Current measurements are done for semiconducting module and voltage measurements are done across the output of each stage. Thresholding is applied to every measured parameter for fault identification. MATLAB simulation is carried out and results are presented. The proposed system for fault detection and redundancy corrective action is capable of taking corrective action within single cycle.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.806</em></p>
EngineeringSolid state transformer (SST)Fault toleranceRedundancyMayuri PoojariP.M. Joshi
Copyright (c) 2024 Mayuri Poojari, P.M. Joshi
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-082024-02-0880680610.62110/sciencein.jist.2024.v12.806Speeded up robust features trailed GCN for seizure identification during pregnancy
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a810
<p>In this work, an efficient computational framework has been designed for seizure identification using MRI analysis. The inputs being brain MRI of pregnant women and corresponding outputs being the seizure or no seizure label. The framework is implemented in two phases. First, the informative speeded up robust features (SURF) are extracted from the MRI. Second, these features are further mapped to a graph convolutional neural network (GCN). The maximal clique is generated out of these intermediate features and subjected to convolutional neural network (CNN) architecture for classification. The maximal clique acts as an efficient tool for representing final and fine-tuned feature points through combined graph convolution and thus contributes towards efficient classification. The designed framework is validated through benchmark dataset images presented by NITRC. Experimental evaluation is made on samples of ‘male’, ‘female’ and ‘female with pregnancy’. The overall rate of accuracy stands at 96%, 95%, and 95% respectively.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.810</em></p>
Biomedical and Pharmaceutical SciencesMRIpregnancyMachine learningseizure identificationEEG signalsbrain MRIConvolutional Neural Network (CNN)Geetanjali NayakNeelaMadhab PadhyTusar Kanti Mishra
Copyright (c) 2024 Geetanjali Nayak, NeelaMadhab Padhy, Tusar Kanti Mishra
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-122024-02-1281081010.62110/sciencein.jist.2024.v12.810Digital forensic analysis of attack detection and identification in private cloud environments for databases
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a798
<p>Today, a growing number of applications are transitioning to the cloud due to its numerous benefits and user-friendly nature. Data plays a pivotal role in the migration, and its security is of paramount importance. Database forensics is a field dedicated to investigating malicious activities carried out by attackers, whether they occur on the front-end or back-end. These activities can be traced through the analysis of various database logs, with network details aiding in the identification of the attacker. In this paper, the authors explore the activity indicators by examining and analysing activity logs, database logs and network packets. Cloud forensics presents the digital investigation for the detection of cyber crimes on cloud. This could include data breaches or identity thefts or modification of databases and attacks on the application server and webserver.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.798</em></p>
Computer Sciences and MathematicsSaasDatabaseCloud ComputingForensic analysisData SecurityVarshapriya JyotinagarBandu Meshram
Copyright (c) 2024 Varshapriya Jyotinagar, Bandu BMeshram
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-242024-01-2479879810.62110/sciencein.jist.2024.v12.798Rapid Recover Map Reduce (RR-MR): Boosting failure recovery in Big Data applications
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a773
<p>The rapid growth of Big Data applications has brought forth unprecedented opportunities for insights and innovation, but it has also exposed the inherent vulnerabilities of data processing pipelines to failures. Hardware glitches, software anomalies, and network interruptions can disrupt the smooth execution of critical tasks, leading to extended downtimes, compromised reliability, and increased operational costs. In response to these challenges, we introduce Rapid Recover Map Reduce (RR-MR), an innovative framework designed to revolutionize failure recovery mechanisms within the context of Big Data applications. RR-MR addresses the shortcomings of conventional Map Reduce frameworks by presenting a novel approach to failure recovery that focuses on expeditious restoration of processing tasks. By leveraging advancements in distributed systems, fault tolerance, and parallel processing techniques, RR-MR introduces a multi-faceted strategy that enhances both the efficiency and reliability of recovery processes.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.773</em></p>
EngineeringMap ReduceFault toleranceCheckpoint mechanismBig dataParallel computingSonika ChoreyNeeraj Sahu
Copyright (c) 2023 Sonika Chorey, Neeraj Sahu
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-142023-12-1477377310.62110/sciencein.jist.2024.v12.773The Orchard Guard: Deep Learning powered apple leaf disease detection with MobileNetV2 model
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a799
<p>The apple crops are susceptible to various diseases that can substantially reduce quality and yield, emphasizing the need for an accurate and automated detection system. The designed model can efficiently detect four different classes of an apple leaf viz. Apple Scab, Black Rot, Cedar rust, and Healthy. The detection has been carried out using a transfer learning approach with different models such as AlexNet, DenseNet121, ResNet-50, and MobileNetV2 as the primary ones. With hyperparameter tuning and by using different optimizer combinations we trained the MobileNetV2 model to achieve the best accuracy. The selected model is trained and fine-tuned on an Apple dataset of 3175 images, leveraging transfer learning from pre-trained models on large-scale image datasets. The designed ‘Orchard Guard’ model has achieved an accuracy of 99.36%. The research findings can help in the selection of a useful model for actual use in orchards and can aid in the creation of effective and precise disease management systems.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.799</em></p>
EngineeringApple Leaf Disease DetectionConvolutional Neural Network (CNN)Deep Learning MobileNetV2Snehal BanaraseSuresh Shirbahadurkar
Copyright (c) 2024 Snehal Banarase, Suresh Shirbahadurkar
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-252024-01-2579979910.62110/sciencein.jist.2024.v12.799In-silico studies of phytoconstituents of Bacopa monnieri and Centella asiatica with Crystal structure of Myelin Oligodendrocyte Glycoprotein against primary demyelination in Multiple Sclerosis
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a764
<p class="05Abstracttext"><span lang="EN-US">There is a rapid spread of Multiple Sclerosis disorder across the globe. There are around 2.8 million cases of Multiple Sclerosis in the world; among them, 1 million are just present in the US. Many drugs have been tested on MS patients but there is no effective treatment for MS till now. Many inhibitors, such as dronabinol, and nabilone, have been used to treat MS. So, we tested different compounds from <em>Bacopa monnieri and Centella asiatica</em> to inhibit the symptoms caused by MS. We targeted the 1PY9 receptor as it has shown some good results in experimental labs. In this article, we will study the binding interactions through the molecular docking model. Our study provided insight into possible treatments for MS during interactions between various bioactive compounds and MS receptors. This study found that Bacosine, Ursolic acid, Betulinic acid, Stigmastanol and Stigmasterol have the potential to inhibit the 1PY9 receptor and their binding energies are -10.12 kcal/mol, -9.52 kcal/mol, -8.95 kcal/mol, -9.93kcal/mol, and -9.51 kcal/mol. Based on bioavailability radar studies, Madecassic acid and Terminolic acid are two bioactive compounds that can be further used in Sclerosis disorders.</span></p> <p class="05Abstracttext"><span lang="EN-US"><em>URN:NBN:sciencein.jist.2024.v12.764</em> </span></p> <p class="05Abstracttext"><span lang="EN-US"> </span></p>
Biomedical and Pharmaceutical SciencesMultiple sclerosisBacopa monnieriCentella asiaticaIn-silico studyMyelin Oligodendrocyte GlycoproteinDemyelinationAaryan GuptaArpita RoyVaseem RajaSarvesh RustagiSumira MalikDevvret Verma
Copyright (c) 2023 Aaryan Gupta, Arpita Roy, Vaseem Raja, Sarvesh Rustagi, Sumira Malik, Devvret Verma
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-022023-12-0276476410.62110/sciencein.jist.2024.v12.764Ultrastructural studies on tendrils of plant climbers reveals a hierarchical tissue organization: A microscopic investigation
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a760
<p>Tendrils are natural morphological designs that function as climbing adaptations adopted by weak stemmed plants to attach over strong stemmed supporting hosts or different artificial support systems. Tendrils are frequently either a modified stem, axillary bud or a modified leaf. Tendril recognises its ideal support hosts with its coiled spring onto which it finally attaches. Robotic engineers are often in the quest for upgraded structures that can sense and grip. The present study focusses on comprehending the underlying structural features that are naturally present in plant tendrils. The study highlights three different types of plant tendrils. Evaluation of plant tendril morphometry is done by various high performance structural analytical tools notably Scanning Electron Microscopy, Atomic Force Microscopy and Fluorescence Microscopy. The ultrastructural features revealed the unique architectural design of cellulose fibrils and tissue level organization in the anatomy which enables the mechanical function of the tendrils. </p> <p><em>URN:NBN:sciencein.jist.2024.v12.760</em></p>
BioSciences and BiotechnologyAtomic force microscopyBioinspirationHierarchical arrangementTendrilsanatomical structureS Narasimhan
Copyright (c) 2023 S Narasimhan
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-11-232023-11-2376076010.62110/sciencein.jist.2024.v12.760Acoustic signal enhancement using autoregressive PixelCNN architecture
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a770
<p>Acoustic Signals such as speech and sound are easily degraded by interferences present in our surroundings.The present work explores the usage of the Pixel CNN architecture for the removal of non-stationary noises from the speech signal. The presence of noise in speech signals affects the performances of applications that use speech signal as a medium for communication such as automatic speech recognition systems, hearing aid, mobile phones. Pixel CNN is a deep generative network architecture implemented as an autoregressive model. The dataset “NOIZEUS” is used for noise mixed speech samples and clean speech samples. The architecture learns the feature from the input speech using the spectrogram representation of speech signal. To prove the efficiency of the method, the performance of Pixel CNN architecture is compared with a number of baseline methods to prove its efficiency. The parameters used for comparison are “PESQ” and “STOI”.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.770</em></p>
EngineeringPixel CNNdeep generative modelauto regressionnon-stationary noisesspeech de-noisingShibani Kar
Copyright (c) 2023 Shibani Kar
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-112023-12-1177077010.62110/sciencein.jist.2024.v12.770Multi Feature fusion for COPD Classification using Deep learning algorithms
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a780
<p>Machine learning (ML) and deep learning (DL) are becoming pivotal for providing solutions to healthcare issues. Due to their accurate and quick forecasting models and discoveries, ML and DL algorithms are being used for disease classification by healthcare experts. Along with life-threatening illnesses like cancer, respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD) have been growing more prevalent and endangering the survival of human society. According to the World Health Organization, COPD will be the third-leading cause of death and the seventh-leading cause of illness globally by 2030. Therefore, early detection and fast treatment are essential. The primary methods for diagnosing COPD need inadequate and pricy spirometer and imaging equipment. In this paper, an attempt is made to determine the severity of COPD disease using ML and DL algorithms using the cough sound of the patient. To extract audio features like Mfcc, Chroma, Contract, Mel, and Tonnetz, we have used the Librosa Python Library. To address the issues of imbalanced dataset, we have used the SMOTE algorithm. To find the most effective multi feature fusion for classifying COPD, numerous experiments have been carried out using various fusions of audio features. For the purpose of evaluating the multi-feature fusion's performance, we have run MLP, CNN, RNN, and LSTM models on fusion of two audio features and three audio features. Results of experiments suggest that the LSTM model with Adam as an optimization function gives 100% training accuracy and 87% testing accuracy for fusion of Mfcc and Mel features. As a result of the fusion of the three features of Tonnetz, Chroma, and Mel, CNN model performs better with training accuracy of 90% and testing accuracy of 82%.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.780</em></p>
Computer Sciences and MathematicsCOPDMulti Feature fusionDeep LearningDisease predicationPinal J. PatelDaksha DiwanKinjal A. PatelShashi RangaNiral J. ModiSamay Dumasia
Copyright (c) 2023 Pinal J. Patel, Daksha Diwan, Kinjal A. Patel, Shashi Ranga, Niral J. Modi, Samay Dumasia
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-182023-12-1878078010.62110/sciencein.jist.2024.v12.780Optimization of process parameters using response surface methodology (RSM) for laccase enzyme production using Aspergillus nidulans in solid state fermentation utilizing agro-industrial waste
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a777
<p>The utilization of microbial laccase for the biological delignification of biomass is regarded as an environmentally sustainable procedure. The present study aimed to optimize the process parameters in order to achieve improved laccase production. Mustard oil cake was utilized as a solid substrate, and Aspergillus nidulans was employed as the fungal strain. The present study investigated the impact of different physical and chemical factors on laccase production, specifically focusing on moisture content, incubation time, nutrient pH, incubation temperature, salt concentration, and additional carbon and nitrogen sources. Additionally, response surface methodology was employed to optimize both the media and process parameters. The highest observed laccase activity was around 6.99 U/ml. The best conditions for the manufacture of laccases were determined through observation. It was found that a moisture content of 100% (w/v), an incubation temperature of 28°C, a pH range of 5-6, and the addition of 6% MgSO4 (w/v) and ammonium chloride as supplementary salts in the production medium resulted in the highest production of laccases. The production medium was incubated for a duration of 96 hours.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.777</em></p>
BioSciences and BiotechnologySolid state fermentationagriculture wastelaccase enzymelignocellulosicresponse surface methodologyAshutosh KhaswalSantosh Kumar MishraNeha ChaturvediPrabir Kumar PaulRavi Kant SinghArpita RoyChetan PanditVaseem RajaDevvret Verma
Copyright (c) 2023 Ashutosh Khaswal, Santosh Kumar Mishra, Neha Chaturvedi, Prabir Kumar Paul, Ravi Kant Singh, Arpita Roy, Chetan Pandit, Vaseem Raja, Devvret Verma
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-162023-12-1677777710.62110/sciencein.jist.2024.v12.777Assessing the efficacy of Machine learning classifier for Android malware detection
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a788
<p>The primary challenges faced by software security experts is the identification and detection of malware within Android applications, as dangerous software is increasingly being embedded in sophisticated manners in application software. The existing applications, as well, are expanding in size and becoming increasingly intricate in terms of their functionalities. The ongoing endeavor of extracting valuable and indicative functionality from applications is a perpetual undertaking. There has been a lack of comprehensive studies that examine the specific attributes designed for identifying malicious applications on the Android platform. This is despite the existence of several feature extraction methods employed in prior research endeavors. Here, a comprehensive and concise analysis is presented to comprehend the behavior of applications using various criteria to identify harmful applications. This study evaluates the efficacy of ten different machine learning classifiers by analyzing a dataset including 15,036 applications categorized as either harmful or benign. The evaluation of classifiers involved the utilization of many metrics like Accuracy, Area Under the Curve (AUC), False Positive Rate (FPR), and False Negative Rate (FNR) towards development of illustrative framework for the detection of Android malware applications.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.788</em></p>
EngineeringAndroid applicationsInternet of Things (IoT)Ensemble learningFeature Extractionmalware detectionreverse engineeringMachine LearningHarshal MisalkarPon Harshavardhanan
Copyright (c) 2024 Harshal Misalkar, Pon Harshawardhanan
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-042024-01-0478878810.62110/sciencein.jist.2024.v12.788An early-stage Alzheimer's disease detection using various imaging modalities and techniques – A mini-review
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a803
<p>Alzheimer's is a disease that affects the brain parts and leads the cells of the brain to die. It is a permanent disorder that causes danger in memory and loss the responsiveness related to the environment. The brain network plays a significant part in the identification of (Alzheimer's Disease) AD and (Mild Cognitive Impairment) MCI disorders. Since the Alzheimer's Association cautioned that Alzheimer’s disease will affect 1 in 85 people by 2050, it is highly essential to have a role play to get a faster diagnosis and a prognosis. The biomarker used to diagnose the disease for distinguishing across various dementia causes needs early detection. Machine learning (ML) uses a variety of techniques to allow (Normal Controls) NCs to benefit from high dimensional data sets. This paper presents a study in early-stage identification or classification of AD using different transferred ML techniques with different modalities and their critical assessment and analysis.</p> <p><em>URN: NBN: sciencein.jist.2024.v12.803</em></p>
Computer Sciences and MathematicsMachine LearningMedical Image AnalysisPredictive analysisAlzheimer's disease detectionT. DeenadayalanS.P. Shantharajah
Copyright (c) 2024 T. Deenadayalan, S.P. Shantharajah
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-052024-02-0580380310.62110/sciencein.jist.2024.v12.803Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a811
<p>A novel Surface Electromyography (sEMG) system has been innovatively designed for individuals with paralysis. This system utilizes EMG technology to detect and interpret muscle signals, translating them into functional control and communication. The process involves signal optimization through a pre-amplifier, noise reduction via an RC filter, and digital conversion using an analog-to-digital converter (ADC). A central microcontroller employs programming to map EMG patterns to actions, creating a direct user-system interface. We have developed a hardware module for testing purposes. The precise manipulation of the hardware module, perfectly aligned with the user's visual objectives, is the result of this complex integration. The suggested method basically creates a sophisticated interface that enables users to intuitively and successfully operate the hardware module through their eye motions, opening up new opportunities for improved interaction and communication. Real-time analysis and command execution enhance user experience, with a user-friendly display providing visual feedback for executed actions. This innovation enhances their quality of life, independence, and social engagement, bridging the gap between paralysis and active participation. Additionally, it holds broader implications for assistive technology and neuroengineering, inspiring further advancements in disability support and rehabilitation. The system's comfort-focused design incorporates fail-safe mechanisms, and its potential applications span communication, environmental control, and artistic expression. A streamlined calibration process enhances user autonomy, and our collaborative approach ensures alignment with clinical needs and daily life requirements.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.811</em></p> <p> </p>
EngineeringEMGEye Movementfacial paralysisbiomedical signalmicrocontrollerYogesh ThakareRahul KadamUtkarsha WankhadeChetan RawarkarPratik K Agrawal
Copyright (c) 2024 Yogesh Thakare, Rahul Kadam, Utkarsha Wankhade, Chetan Rawarkar, Pratik K Agrawal
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-122024-02-1281181110.62110/sciencein.jist.2024.v12.811Outlier detection and imputation of missing data in stock related time series mulitivariate data using LSTM autoencoder
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a761
<p>Incomplete data is a well-known problem with large databases, which raises challenges for many data mining applications. The main focus will be on developing scalable and adaptable anomaly detection techniques that can spot unusual trade patterns in vast amount of stock related data. In the designed method, an autoencoder based unsupervised outlier detection for multivariate time series data has been trained within a Long Short Term Memory Model, shaped by deep learning networks. Further, we also estimated value of outlier which is treated as missing value in dataset using our designed algorithm. The suggested method demonstrates the value of feature selection and data preparation for creating effective techniques for data modeling. Deep learning models can be tuned very little to produce good results. Our work used the Stock related dataset, that many investors choose due to its high risk, high reward, and flexible trading. In comparison, our designed work also examines the statistics and machine learning models in related application fields.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.761</em></p>
Computer Sciences and MathematicsDeep LearningFeature ExtractionStock MarketMachine LearningSwati JainNaveen ChaudharyKalpana Jain
Copyright (c) 2023 Swati Jain, Naveen Chaudhary, Kalpana Jain
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-012023-12-0176176110.62110/sciencein.jist.2024.v12.761Phytoplankton analysis and assessment of reservoir water quality at Rena Medium Reservoir, Godavari Basin, India
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a784
<p>One of the greatest environmental issues is phytoplankton development in reservoirs. Reservoirs, and other bodies of water used for residential uses with minimal water circulation support the most intense phytoplankton development. This process results in a severe decrease in potable water quality and an increase in the overall amount of hazardous chemicals in water. The variance in the growth of phytoplankton species must be determined to monitor the status of water quality. Moreover, an appropriately designed and operating system of data collection and analysis is urgently required for the improvement of the existing setup for water quality monitoring of reservoirs. The present investigation is conducted to know the rate of variation in the phytoplankton population of 'Rena Medium Reservoir', Godavari Basin, Maharashtra, India. The research analysis and assessment is conducted for a period of six months from December 2021 to May 2022 comprising winter and summer seasons with five sampling sites in three different zones of the reservoir. The water quality is affected by dilution during monsoon and the high evaporation rate during summer. There is fluctuation in the reservoir water quality at the same monitoring locations. These variations are attributed to the increasing influence of anthropogenic activities. This analysis assessment helps the authorities to identify the best and most sustainable uses of the reservoir and decide the future course of the Godavari River Basin.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.784</em></p>
Environmental ScienceWater Resource ProjectRena RiverRena Medium ReservoirGodavari BasinPhytoplankton AnalysisWater QualityGopal Malba AlapureParmeshwar Narayan WalseOmprakash Sugdeo RajankarBharat Manohar Shinde
Copyright (c) 2023 Gopal Malba Alapure, Parmeshwar Narayan Walse, Omprakash Sugdeo Rajankar, Bharat Manohar Shinde
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-262023-12-2678478410.62110/sciencein.jist.2024.v12.784Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a805
<p>The IoT is transforming healthcare by enabling extensive connectivity between medical professionals, equipment, staff, and patients, facilitating real-time monitoring. While the network's scale and diversity offer advantages for data exchange, they also pose challenges for privacy and security, particularly with sensitive medical information. To address this, deep learning-based cryptographic and biometric systems are utilized for authentication and anomaly detection in medical systems. However, power constraints on network sensors necessitate efficient security schemes. Thus, the authors propose a novel framework, the deep Inception-ResNetV2 with privacy preservation, to secure data transmission while minimizing encryption and decryption time. Implementing this method reduces the network's burden, saving time and costs in communication. Compared to alternatives like private biometric-based authentication, this model demonstrates superior performance.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.805</em></p>
Computer Sciences and MathematicsPatient DataData SecurityInception-ResnetV2Cryptographic TechniquesInternet of Things (IoT)N.V. Rajasekhar ReddySwathi BaswarajuP. Mary Kamala KumariPhanikanth ChintamaneniB. Raveendra NaickB. Gunapriya Pradhan
Copyright (c) 2024 N.V. Rajasekhar Reddy, Swathi Baswaraju, P. Mary Kamala Kumari, Phanikanth Chintamaneni, B. Raveendra Naick, B. Gunapriya Pradhan
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-02-072024-02-0780580510.62110/sciencein.jist.2024.v12.805Dual-Port MIMO antenna design for IoT: Analysis and implementation
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a768
<p>The research presents a new type of antenna that makes use of MIMO technology, which employs multiple antennas for signal transmission and reception. This antenna also features a defective ground structure, a patterned ground plane that is frequently utilized to boost an antenna's performance. Adding or altering the ground plane patterns can enhance the antenna's performance. In order to improve antenna performance, a variety of designs, including rectangular and asymmetrical shapes, are being investigated, indicating a flexible method of antenna design optimization. The following features apply to the proposed MIMO antenna: 60 x 60 mm2 in size, two ports are present. Operating Band: 1-6 GHz, Gain: 6.40 dBi, Return Loss (RL): -30.06 dB. The antenna is made to work in the 1-6 GHz frequency band, which includes a large variety of wireless communication frequencies. It specifically indicates appropriateness for WLAN frequencies such as 1.2 GHz, 2.4 GHz, 5.2 GHz, and 5.98 GHz that are used in IoT applications. A single radiating element with two slits and enhanced rectangular patches are features of the antenna's design. It can support sub-6 GHz WLAN bands as a result, making it appropriate for IoT applications that use these frequencies. In this research, a MIMO antenna with a DGS is shown. It has good performance characteristics, such as a broad operating band and applicability for IoT applications in different WLAN frequency bands. This antenna design might be useful for wireless communication systems needing multiport capabilities and increased performance.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.768</em></p>
EngineeringAntenna arrayDefected Ground Structure (DGS)GainInternet of Things (IoT)WLAN (Wireless Local Area Network)Kuldeep PandeyRitesh Sadiwala
Copyright (c) 2023 Kuldeep Pandey, Ritesh Sadiwala
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-082023-12-0876876810.62110/sciencein.jist.2024.v12.768Performance study on cold-formed steel structural frame made with lipped channel column and beam elements subjected to lateral loading
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a791
<p>This paper presents a performance study of cold-formed steel (CFS) structural frames subjected to lateral loading. The study focuses on performance based on the experimental and numerical study of cold formed steel structural frames, investigating strength and stiffness. The cold-formed steel frame consists of three storey structural frames that are made with cold-formed steel channel sections of columns and beam elements. The study involves analytical investigation via ANSYS workbench software. It was used to perform FE analysis and examine the factors influencing the failure of the frame. The result showed that the column made with lipped channel revealed stiffness higher than column made with normal cold formed steel. Moreover, increasing the thickness of structural elements leads the better lateral force resisting capacity of the frame.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.791</em></p>
EngineeringCivil EngineeringCold-formed steel (CFS)Lateral loadingFinite element analysis (FEA)Structural Building FrameLoad-displacement relationshipLight-gauge constructionS. SozharajaRajaram Baskar
Copyright (c) 2024 S. Sozharaja, Rajaram Baskar
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-092024-01-0979179110.62110/sciencein.jist.2024.v12.791Chemically modified Ginger and Spirulina for bioremediation of Hexavalent Chromium from polluted water
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a782
<p>Heavy metals are toxic pollutants that are persistent in air, water and soil. These metals are non-biodegradable and cause variety of disorders due to their bioaccumulation in living organisms. Chromium is one such hazardous heavy metal found in effluents of tanneries, electroplating industries, etc. The current study presents natural biosorbents and their chemical modification and immobilization as a low-cost alternative for uptake of hexavalent Chromium. Ginger (<em>Zingiber officinale</em>) and <em>Spirulina maxima </em>are used as biosorbents in the current study. The physicochemical parameters required for biosorption were optimized by batch adsorption experiments to attain maximum adsorption of upto 70% in lake waters of Bangalore city. The maximum adsorption obtained by <em>Z. officinale</em> - 38%, 78%, 88% and <em>S. maxima</em>, 22%, 48%, 39% was obtained using natural, chemically modified and immobilized biosorbent respectively in lake water. Treatment in lake water not only reduced the metal concentration but also mitigated the oxidative stress in the liver homogenates of zebrafish, depicting the effectiveness of bioremediation methodology and biosorbent used in the current study.</p> <p><em>URN:NBN:sciencein.jist.2024.v12.782</em></p>
BioSciences and BiotechnologyZebrafishProtein carbonylsBiosorptionbioremediationbiosorbentschromiumheavy metalSpirulina maximaZingiber officinaletoxicityBhanu Revathi KurellaAishwaraya Srinivasa RamanujanBhagyashree NagarajRajeswari NarayanappaShinomol George Kunnel
Copyright (c) 2023 Bhanu Revathi Kurella, Aishwaraya Srinivasa Ramanujan, Bhagyashree Nagaraj, Rajeswari Narayanappa, Shinomol George Kunnel
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-202023-12-2078278210.62110/sciencein.jist.2024.v12.782Analysis of vertical pressure vessel with skirt support
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a792
<p class="05Abstracttext">Emphasizing safety has been the top priority in the design of pressure vessels because of their frequent exposure to high-pressure, high-temperature conditions and potential containment of hazardous materials. It is essential to make sure that these containers are impervious to leaks and strong enough to resist the rigors of their operational environment. This study aims to investigate the exact design of a vertical pressure vessel (PV) in accordance to Section VIII, Division 1 of the Boiler and Pressure Vessel (B&PV) Codes of the American Society of Mechanical Engineers (ASME). The study considered various loads by following the UG-22 criteria. Utilizing a 2D axisymmetric model in ANSYS APDL, we conducted a comprehensive simulation to assess the vessel's performance. Following Section VIII, Division 2 of the ASME B&PV Codes, our evaluations cover structural, thermal, and thermo-structural aspects to prevent plastic collapse. Special attention is given to the junction of the vessel's skirt and head, identified as a zone with elevated temperature (Hot Box). This region was subjected to Extensive finite element analysis (FEA) to ensure that the stress levels remain in the acceptable bounds.</p> <p class="05Abstracttext"><em>URN:NBN:sciencein.jist.2024.v12.792</em></p>
EngineeringHarmonic elementsPressure VesselStress linearizationThermal analysisHot BoxThermo-structural analysisBharat Murlidhar ShindeTushar Ankush JadhavOmprakash Sugdeo RajankarGopal Malba AlapureAkash Prakash Choudhari
Copyright (c) 2024 Bharat Murlidhar Shinde, Tushar Ankush Jadhav, Omprakash Sugdeo Rajankar, Gopal Malba Alapure, Akash Prakash Choudhari
https://creativecommons.org/licenses/by-nc-nd/4.0
2024-01-092024-01-0979279210.62110/sciencein.jist.2024.v12.792Industrial measurement system with LoRa interface
https://pubs.thesciencein.org/journal/index.php/jist/article/view/a783
<p>Measurement, monitoring, and controlling various process parameters is a practice that has been prevalent in the manufacturing industry for the past few decades. Numerous measurement instruments which are available in the industry suit only a particular application requirement. Thus, multiplexing of a measurement device has many advantages over a single parameter measurement device. Wireless connectivity with the application infrastructure has always been desirable since the advent of IoT. This paper showcases the design process for the development of such a multiplexed measurement system, that will be compatible with a wide range of applications, with minimal installation space and cost, and which can communicate with the IoT network with the help of a LoRa module. LoRa, a wireless modulation method, enables low-power devices to communicate across long distances and low bit rates. The design of industrial measurement with LoRA interface aims to be a portable stand-alone embedded system, with an attractive and easily interpretable Graphical User Interface (GUI).</p> <p><em>URN:NBN:sciencein.jist.2024.v12.783</em></p>
EngineeringMetrologySensor interfaceData loggingInternet of Things (IoT)Industrial IoTIndustry designManasi NatuSupriya RajankarOmprakash RajankarVrushali Raut
Copyright (c) 2023 Manasi Natu, Supriya Rajankar, Omprakash Rajankar, Vrushali Raut
https://creativecommons.org/licenses/by-nc-nd/4.0
2023-12-262023-12-2678378310.62110/sciencein.jist.2024.v12.783