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Enhancing Wildlife Monitoring Through Infrared Imaging: A Deep Learning Approach for Improved Animal Region segmentation and species Identification

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Journal of Harbin Institute of Technology(New series) 2024年

作者： v Janani C shanthi Department of Computer science， school of Computing sciences， vels Institute of science Technology and Advanced studies

Infrared imaging has proven to be a powerful tool in various fields， with particular significance in wildlife monitoring. In this paper， we present an extension of our previous research， focusing on advancing the segmentation of animal regions in enhanced infrared images and expanding the scope to include species identification. Our proposed methodology builds upon the success of the R-CNN (Region-based Convolutional Neural Network) object detection to improve the accuracy and robustness of animal region segmentation， while simultaneously extending our model’s capabilities to identify and classify the species within those regions. By fine-tuning the R-CNN model on a larger dataset that includes annotated infrared images and species labels， we enhance its capacity to not only accurately segment animal regions but also classify them into specific species categories. To assess the performance of our extended model， we employ a comprehensive set of evaluation metrics， including pixel-based metrics like Intersection over Union (IoU)， as well as species classification accuracy. Our results demonstrate significant improvements in both region segmentation accuracy and species identification compared to our previous work and existing methods. This research showcases the potential of deep learning techniques， combined with transfer learning， to advance wildlife monitoring applications using infrared imaging.

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Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

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Computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： v.thanikachalam.s.shanthi K.Kalirajan sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar sri sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,squeezeNet model is employed as a feature extractor in which the hyperparameters of squeezeNet are tuned using Oppositional Glowworm swarm Optimization(OGsO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGsO algorithm for squeezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

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Artificial neural network with modified rider optimization for design and control of Pv-integrated quasi Z-source cascaded multilevel inverter system

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International Journal of Modeling, simulation, and scientific Computing 2021年第1期12卷 87-111页

作者： v.C.Harish Kumar s.Amala shanthi Department of EEE NICHE ThuckalayKanyakumariTamil NaduIndia Department of ECE NICHEThuckalayKanyakumariTamil NaduIndia

The modeling of the controller for quasi Z-source Cascaded Multilevel Inverter(qZsCMI)-dependent 3-phase grid-tie Photovoltaic(Pv)power system is considered in this *** state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived ***,such processes are limited to known system models,which are uncertain in future ***,the controller for 3-phase qZs-CMI is modeled based on two phases,and the source Pv voltage and output grid current are *** Phase I,optimized Proportional Integral(PI)controller is used for finding out the total Pv voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid *** two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is ***,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid *** two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed *** main objective of phase I is to lessen the error among the reference Pv voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid ***,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZs-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.

关键词： qZs-CMI PI controller PR controller artificial neural network fitness enabled-rider optimization algorithm

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Reckoning the sIX1 mutation＇s effects in branchio-oto-renal syndrome - A bioinformatics approach

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Frontiers in Biology 2015年第5期10卷 448-457页

作者： B. Preethi v. shanthi K. Ramanathan Industrial Biotechnology Division School of Bio Sciences and Technology VIT University Vellore - 632014 Tamil Nadu India

Branchio-oto-renal syndrome （BOR） is autosomal dominant disorder which generates hearing impairment and kidney failures in affected individuals. The disease genomic maps were drawn back in recent years, demonstrating, missense mutations responsible in disease were located in sIX1, EYA1 and EYA2 genes. We try to uncover molecular biology of the syndrome with bioinformatics perspective, taking s1X1 and EYA2 protein interaction at center point. The study initiated with 23 natural mutations of sIX1 gene. They were first analyzed with prediction servers like sIFT, PolyPhen2, I Mutant, sNPs＆GO, PHD-sNP and Panther, to identify their impact on their structural stability and function. subsequently it narrowed down to seven consistent with our quest. They were analyzed on IUPred disorder prediction server. Later sIX1 and its all mutant proteins were docked with EYA2 protein using GRAMM-X server. The binding affinity of docked structures was analyzed using DFIRE2 algorithm. The results justify the earlier wet laboratory studies and indicate the reason behind them. Finally we summarize that the proven inactivity of all other mutants is due to the structural disorder created by mutations, hence usual molecular interaction is hindered; strangely protein interaction takes place at DNA binding site of sIX1 mutants.

关键词： branchio-oto-renal syndrome (BOR) hearing loss damaging mutations sIX1 EYA2 protein-protein interactions

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Computational analysis of deleterious missense mutations in aspartoacylase that cause Canavan's disease

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science China(Life sciences) 2012年第12期55卷 1109-1119页

作者： sREEvIsHNUPRIYA K. CHANDRAsEKARAN P. sENTHILKUMAR A. sETHUMADHAvAN R. shanthi v. DAIsY P. NIsHA J. RAMANATHAN K. RAJAsEKARAN R. Bioinformatics Division School of Biosciences and Technology Vellore Institute of TechnologyVellore 632014 Tamil Nadu India Department of Biotechnology Holy Cross CollegeTrichy 620002 Tamil Nadu India

In this work, the most detrimental missense mutations of aspartoacylase that cause Canavan's disease were identified computationally and the substrate binding efficiencies of those missense mutations were analyzed. Out of 30 missense mutations, I-Mutant 2.0, sIFT and PolyPhen programs identified 22 variants that were less stable, deleterious and damaging respectively. subsequently, modeling of these 22 variants was performed to understand the change in their conformations with respect to the native aspartoacylase by computing their root mean squared deviation (RMsD). Furthermore, the native protein and the 22 mutants were docked with the substrate NAA (N-Acetyl-Aspartic acid) to explain the substrate binding efficiencies of those detrimental missense mutations. Among the 22 mutants, the docking studies identified that 15 mutants caused lower binding affinity for NAA than the native protein. Finally, normal mode analysis determined that the loss of binding affinity of these 15 mutants was caused by altered flexibility in the amino acids that bind to NAA compared with the native protein. Thus, the pre- sent study showed that the majority of the substrate-binding amino acids in those 15 mutants displayed loss of flexibility, which could be the theoretical explanation of decreased binding affinity between the mutant aspartoacylases and NAA.

关键词： missense mutation Canavan's disease aspartoacylase NAA flexibility

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