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A Comprehensive Meta-Analysis on Animal Identification Using Machine Learning and Deep Learning
Artificial Intelligence (AI)-based models have shown promising results in the identification of animals breeds. The surge in the development of new models has opened up new avenues for computer vision. The growing need to achieve cent percent accuracy in the prediction, identification and classification of data/images has motivated researchers to develop innovative strategies seamlessly. The results of various AI models are analyzed in terms of their classification accuracy. EfficientNet-B0 provided an accuracy of 95% in cat breed identification. InceptionV3 deep learning model reached the maximum accuracy of 96.75%, 96.57%, and 100% on dog, goat, and pig breed identification, respectively. ResNet attained an accuracy of 85.77% on snake species identification. This article provides an in-depth analysis of animal classification/species identification models. The inferences drawn out of this literature review would help the researchers in the selection of an ideal AI model to develop an automated animal classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife
The classification of animal sounds has emerged as a vital tool in contemporary research, offering numerous benefits for animal occurrence records, taxonomic research, and behavioral studies. However, the problem of accurately identifying animal species based on their vocalizations remains a significant challenge, particularly in real-world environments where background noise and variability in sound patterns can hinder classification accuracy. In this paper addressed this challenge by proposing a CNN-optimized approach for classifying animal sounds. In order to enhance the number of sound samples, utilized augmentation techniques to extract animal sounds from the Kaggle animal sounds dataset. The animal sounds totally 600 audio samples are used. To improve performance, this model was developed using feature extractions from the MFCC, ZCR, and Mel-Spectrogram. The seamless deployment of forest department workers is ensured by the interpretability of our model for real-world applications related to wildlife conservation and monitoring. The main goal is to successfully identify animals using auditory properties, such as tiger, leopard, elephant, and otter noises, based on their vocalizations. Additionally, The optimized CNN and LSTM for sound classification. The Optimized CNN outperformed all other models, achieving an outstanding 98.32 % training accuracy rate. 2025 IEEE. -
Effective Models for Computing Optimized Storage Systems for Energy
This chapter investigates effective modeling techniques for designing optimized storage systems that minimize energy consumption. We explore various models capturing the interplay between storage performance, capacity, and energy efficiency, focusing on computational methods to enhance effectiveness. As the demand for renewable energy sources continues to increase, the need for reliable and efficient storage solutions becomes increasingly crucial. We discuss the design and implementation of optimized storage systems for energy, highlighting computational models role in improving efficiency. Starting with an overview of the energy storage system, we examine different modeling approaches such as mathematical optimization, machine learning, and simulation techniques. Each approach offers a unique approach to addressing the complexities of energy storage. Additionally, we discuss optimization models, ensuring that energy storage solutions are both technically efficient and economically viable. In summary, this section emphasizes the importance of computational modeling in developing efficient energy storage systems, which are crucial for meeting energy integration demands and ensuring stability and sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart Intelligence Aided Power and Energy Management
The Artificial Intelligence (AI) has become a revolutionary technology in power and energy management, providing exceptional prospects for improving efficiency, reliability, and sustainability. This study delves into the incorporation of AI methodologies into smart intelligence-driven systems for power and energy management. It delves into how AI algorithms, encompassing machine learning and optimization approaches, are utilized to enhance energy generation, distribution, and consumption across a range of environments, including smart grids, microgrids, and intelligent buildings. The abstract examines the primary challenges and factors to consider when implementing AI-driven solutions for power and energy management, which encompass issues such as data quality, privacy, security, and scalability. It emphasizes the crucial role of transparency and interpretability in AI algorithms to cultivate trust among stakeholders and secure user acceptance. Additionally, it addresses the importance of upholding ethical standards and regulatory requirements to address societal apprehensions and mitigate potential risks linked to the deployment of AI in energy systems. Moreover, the abstract highlights AIs contribution to advancing energy efficiency and sustainability through dynamic demand response, incorporating renewable resources, and the optimization of grid operations. It underscores the importance of on-going monitoring and evaluation of AI-driven energy management systems to pinpoint areas for enhancement and mitigate unintended repercussions. In summary, this paper offers perspectives on AIs potential to transform power and energy management methodologies, leading to more intelligent, robust, and eco-friendly energy systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation
Machine Learning Algorithms are widely used in the contemporary era of highly compatible technical improvements to provide answers to the challenges of business environment, yet crucial services for a firm to run successfully in this intensely competitive E-commerce sector. Recently, strategies like clustering and classification mechanisms that allow for the classification of both existing and new clients into clusters have also produced positive outcomes. Recency, Frequency, and Monetary (RFM) measures are hugely being used these days to perform these kinds of tasks. In this study, individual one-dimensional clustering on the Recency, Frequency, and Monetary columns was performed, and a weighted average or preferred linear combination of the three features was then used to calculate an overall score. Summing up the result of three individual clusters. Finally, all of the distinct clients were divided into these three segments based on the overall score, which was divided into three categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Smart Systems for Disease Prediction: Advancements, Applications and Challenges
Smart Systems for Disease Prediction: Advancements, Applications and Challenges is a comprehensive book that explores the use of intelligent technologies to predict diseases accurately and efficiently. It covers a wide range of topics, including image and signal processing, behavioural analysis, and the integration of multimodal data in healthcare. The book examines the application of artificial intelligence, machine learning, and data analytics in creating predictive models for diseases. It also addresses the challenges, ethical considerations, and future directions in the field. This work emphasises the significant impact of intelligent systems on enabling early diagnosis, personalised medicine, and improving patient outcomes. 2026 S. Vijayalakshmi, Alwin Joseph, Naived George Eapen, Balamurugan Balusamy, Jagjit Singh Dhatterwal, and Kuldeep Singh Kaswan. -
Advances in Carbon-Element Bond Construction under Chan-Lam Cross-Coupling Conditions: A Second Decade
Copper-mediated carbon-heteroatom bond-forming reactions involving a wide range of substrates have been in the spotlight for many organic chemists. This review highlights developments between 2010 and 2019 in both stoichiometric and catalytic copper-mediated reactions, and also examples of nickel-mediated reactions, under modified Chan-Lam cross-coupling conditions using various nucleophiles; examples include chemo- and regioselective N-arylations or O-arylations. The utilization of various nucleophiles as coupling partners together with reaction optimization (including the choice of copper source, ligands, base, and other additives), limitations, scope, and mechanisms are examined; these have benefitted the development of efficient and milder methods. The synthesis of medicinally valuable or pharmaceutically important nitrogen heterocycles, including isotope-labeled compounds, is also included. Chan-Lam coupling reaction can now form twelve different C-element bonds, making it one of the most diverse and mild reactions known in organic chemistry. 1 Introduction 2 Construction of C-N and C-O Bonds 2.1 C-N Bond Formation 2.1.1 Original Discovery via Stoichiometric Copper-Mediated C-N Bond Formation 2.1.2 Copper-Catalyzed C-N Bond Formation 2.1.3 Coupling with Azides, Sulfoximines, and Sulfonediimines as Nitrogen Nucleophiles 2.1.4 Coupling with N, N -Dialkylhydroxylamines 2.1.5 Enolate Coupling with sp 3-Carbon Nucleophiles 2.1.6 Nickel-Catalyzed Chan-Lam Coupling 2.1.7 Coupling with Amino Acids 2.1.8 Coupling with Alkylboron Reagents 2.1.9 Coupling with Electron-Deficient Heteroarylamines 2.1.10 Selective C-N Bond Formation for the Synthesis of Heterocycle-Containing Compounds 2.1.11 Using Sulfonato-imino Copper(II) Complexes 2.2 C-O Bond Formation 2.2.1 Coupling with (Hetero)arylboron Reagents 2.2.2 Coupling with Alkyl- and Alkenylboron Reagents 3 C-Element (Element = S, P, C, F, Cl, Br, I, Se, Te, At) Bond Forma tion under Modified Chan-Lam Conditions 4 Conclusions. 2021 Georg Thieme Verlag. All rights reserved. -
Synthesis and Nuclear Magnetic Resonance Studies of 2-Thiophenecarboxaldehyde Nicotinic Hydrazone and 2-Thiophenecarboxaldehyde Benzhydrazone
Synthesis and NMR spectral studies of bidentate N and S heterocycles of 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone have been carried out. The compounds, 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone were synthesized by reacting stoichiometric quantities of nicotinic hydrazide and benzhydrazide with 2-thiophene carboxaldehyde in methanol in the presence of glacial acetic acid at refluxing temperature. Upon cooling the reaction mixture, the products were obtained as colorless solids. 1H, 13C, 1H-1H COSY, and 1H-13C HSQC experiments have been conducted to characterize the compounds. 2020 Malaysian Institute of Chemistry. All rights reserved. -
Paramagnetic mononuclear oxovanadium(IV) complex as oxidation catalyst
2-Thiophenecarbanicotinic hydrazone is added with equimolar mixture of vanadyl acetyl acetonate in methanol to obtain oxovanadium(IV) complex of 2-thiophenecarbanicotinic hydrazone. Oxovanadium(IV) complex of 2-thiophenecarbanicotinic hydrazone is acted as an effective catalyst in the process. The catalytic reactions were carried under room temperature. The products generated were benzil and furil. The influence of solvent, oxidant and quantity of catalyst has been investigated. Oxovanadium(IV) complex of 2-thiophenecarba-nicotinic hydrazone proves significantly higher catalytic activity towards oxidation of secondary alcohols to ketones. The catalyst was proved to be very effective due to its recovery by simple filteration after completion of the reaction. It was reused several times which suggests that there is no change in the catalytic efficiency. Oxovanadium(IV) complex of 2-thiophenecarbanicotinic hydrazone did not show any leaching during the reaction, confirmed the heterogeneous nature. 2018 Chemical Publishing Co. All Rights Reserved. -
Synthesis, characterization, magnetic, thermal and electrochemical studies of Oxovanadium(IV) Complex of 2-thiophenecarba benzhydrazone
The hydrazone ligand obtained from 2-thiophene carboxaldehyde and benzhydrazide react with an equimolar mixture of vanadyl acetyl acetonate in methanol to yield oxovanadium(IV) complex of 2-thiophenecarba benzhydrazone. The prepared compound shows effective solubility in organic solvents like acetonitrile, DMF and DMSO. Molar conductivity data of oxovanadium( IV) complex of 2-thiophenecarba benzhydrazone revealed its nonelectrolytic behavior in DMF and DMSO. EPR spectra of 2-thiophenecarba benzhydrazonato oxovanadium(IV) was recorded in DMF at LNT and g and A values were calculated. The complex was proposed to be square pyramidal in geometry. Cyclic voltammograms of the complex in DMF were studied by changing the scan rates 50, 100, and 200 mV/s. ? E values of the complex showed the reversible criterion and ipc/ipa values which were close to 1 indicating the redox couple as reversible. Thermograms of the complex were recorded to find the weight loss at different temperature ranges. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectra showed mass number of the molecular ions. 2018 River Publishers. -
Examining Malabar through the Lens of a Geo-body
The textual traditions and Malabars geographical extent have been diversely perceived from one period to another. Despite the regions well-established maritime historiography, this evolution hardly captured historians interest. The paper examines the geo-body of Malabar and its evolution as a historiographical region from its earliest textual reference to contemporary ones, navigating its politico-economic networks and cultural and intellectual landscape vis-vis the regions strategic location as an Indian Ocean littoral. 2025, Economic and Political Weekly. All rights reserved. -
Anti-inflammatory and anti-diabetic activities of lanthanum oxide nanoparticles using Plectranthus amboinicus leaf extract
Lanthanum oxide nanoparticle (La2O3 NPs) were synthesized by co-precipitation technique using Plectranthus amboinicus (P. amboinicus ) leaf extract. A yellowish green color was observed after the addition of leaf extract to the NaOH solution. The synthesis of nanoparticles plays a vital role in the field of science and technology. The cubic structure of the La2O3 was confirmed by Powder XRD. The functional groups present in the NPs were confirmed by FT-IR spectroscopy. The absorbance spectrum was observed at 274nm in the wavelength range of 230850nm. The calculated band gap value was 4.32eV. The structural morphology of La2O3 NPs was observed as cubic and irregular shape obtained from SEM image and EDAX spectrum which confirms the presence of La and O elements. The average particle sizes of the NPs were observed to be 40.22nm analyzed by high resolution transmission electron microscopy (HR-TEM) analysis. The main objective of this research work was focused on prepared La2O3 NPs act as a potential inhibitor to handle various inflammations and diabetes problems. Bovine serum albumin (BSA) denaturation approach produced a strong anti-inflammatory response with 92.89% inhibition at 500g/mL while ?-amylase showed significant antidiabetic activity of about 94.55% inhibition at 500g/mL. These results suggest that the green synthesized nanoparticles can be used for ?-amylase and BSA denaturation inhibitory activities, which may be crucial for biomedical applications. The Author(s), under exclusive licence to Society for Plant Biochemistry and Biotechnology 2026. -
Effectiveness of internet-delivered dialectical behavior therapy skills training on executive functions among college students with borderline personality traits: a non-randomized controlled trial
Given the enormous influence of emotions on cognitive processes, individuals with borderline personality disorder (BPD) suffer from marked deficits in higher-order thinking abilities. Considering the prevalence of BPD among college students, this study aimed to investigate the changes in perceived executive functioning among college students with traits/presence of BPD undergoing internet-delivered dialectical behavior therapy skills training (DBT-ST) that included the mindfulness and emotion regulation modules. An internet-delivered version of DBT-ST was opted for, as technological advancements in the present era promote the use of online platforms for psychotherapy. This non-randomized controlled trial consisted of 36 college students with traits/presence of BPD. The intervention group attended 13 sessions of DBT-ST, and the control group attended 13 sessions of behavioral activation. Perceived executive functioning was assessed using the Behavior Rating Inventory of Executive Functions for Adults. A 2-way repeated measures analysis of variance was used to evaluate the treatment impact on the outcome variable. Results showed that the DBT-ST group had larger improvements in their abilities to initiate, plan, and organize current and future-oriented task demands and to organize their everyday environment, compared to the control group. Both, the DBT-ST group and the control group demonstrated improvements in emotional control, working memory, and their abilities to shift and task monitor. Findings suggest that the internet-delivered version of DBT-ST, consisting of the mindfulness and emotion regulation modules, can foster notable improvements in executive functions among college students with traits/presence of BPD. Improved executive functioning is one of the several multifaceted outcomes of dialectical behavior therapy. Copyright: the Author(s), 2023. -
Effectiveness of dialectical behavior therapy as a transdiagnostic treatment for improving cognitive functions: a systematic review
Dialectical behavior therapy (DBT) has been found to be an efficacious treatment for disorders characterized by high levels of emotional instability. In view of the multifaceted applications of DBT and the extent to which mental disorders can incapacitate cognitive functions, the current systematic review aimed to investigate the effect of DBT in strengthening cognitive functions across various mental health conditions. Original research studies employing both experimental and quasi-experimental designs were included in the review. The literature search was done using different electronic databases, from the first available literature until June 2022, that covered an approximate period of ten years. Joanna Briggs Institute checklist was used to assess the methodological rigor of the studies. Twelve studies conducted on adolescents with emotional dysregulation, and adults with borderline personality disorder, bipolar disorder, attention deficit hyperactivity disorder, and multiple sclerosis were selected. Results indicate that DBT has the potential to improve key cognitive functions such as attention, memory, fluency, response inhibition, planning, set shifting, tolerance for delayed rewards and time perception, as assessed by neuropsychological tests, self-report of cognitive functions, and neuroimaging techniques. Considering the review's findings that showcase the effectiveness of DBT in fostering improvements in cognitive functions, DBT may possibly be chosen as a preferred treatment to ensure that patients reach optimal levels of cognitive functioning. Limitations include lack of sufficient studies encompassing all the common mental health conditions, usage of neuroimaging techniques as only an indirect measure of cognitive functioning and nuances related to the quality of individual studies. Author(s), 2023. -
Task-based Autoethnographic Pedagogical Approach: a phenomenological inquiry into online learning of Critical Food Studies courses
The disengaging experiences reported in the online mode of learning have resulted in considerable deliberations highlighting the need for pedagogical innovations. Therefore, it is crucial to rethink these ideas and develop pedagogical approaches that accommodate a dynamic understanding of learning spaces and meet the demands of the teachinglearning environment of the contemporary period. This study discusses the various steps through which the task-based autoethnographic pedagogical approach (TAPA) was implemented in an undergraduate-level Critical Food Studies course and proposes it as an effective approach to administering certain courses by enabling active learning in the online mode. The study captures learners perceptions of meaningful online learning experiences by using an interpretative phenomenological approach, mapping the aspects that contribute to a sense of rekindled interest and involvement in the course. Some of the dominant patterns that emerge from this phenomenological study are (1) appreciation towards praxis-based online learning, (2) recognition of lived space as a ripe site for inquiry and learning, (3) a heightened sense of engagement with lived contexts, and identity discourses, (4) learners negotiations with TAPA, and (5) learner as an active agent and curator of knowledge. Thus, while situating TAPA as an effective pedagogical approach for online learning and Critical Food Studies curriculum, it is also posited as an approach that initiates negotiation with the epistemic hierarchies within academia. Education Research Institute, Seoul National University 2022. -
What is Remembered in Pandemic: A Commentary on the Mediated Memories of Piety in COVID-19
The paper explores how the experiences of the present pandemic are shaped by the memories of popular religious piety during past pandemics and epidemics. Taking insights from the works of Astrid Erll and Reinhart Koselleck, the process remembering-imagining system within the context of the pandemic is discussed by tracing the reemergence of pandemic deities and narratives of piety in India. Using digitally documented and disseminated narratives on piety emerging during COVID-19, an attempt is made to understand how these narratives shape the experiences, responses, and collective memory of the pandemic. Through a discussion of the shift in the imagination of political leadership and the moral responsibilities of the community, an attempt is made to highlight the mode in which the narratives on piety shape the contours of a time that is otherwise unimaginable. The mediated memories of popular religious piety make it possible to remember similar crisis times and to imagine and reinstate the social order that is threatened by this sudden unimaginable crisis. The paper thus argues that within the context of India, popular religious piety, though often overlooked, becomes a significant part of making sense and shaping the experiences of the pandemic time. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Rhetorics in/of English language education in India: A case of digital natives in higher education programs
The study briefly analyzes the ELT situation in India which is replete with challenges emerging from the lack of engagement with the phenomenon of digitality that further shapes the existing nature of learning and the needs of the learner. After locating the position of English Language in the new education policy of India, the paper discusses the General English (GE) courses offered at undergraduate level at the city of Bangalore in India, thereby shedding light on the existing gaps between policy and practice. It is based on this conjecture that the paper suggests the possibility of introducing rhetorical practices in GE courses at undergraduate levels in various institutions in urban India. In order to substantiate this suggestion, the results of a survey conducted with the learners (N=359) of a GE course based on rhetorics at a Southern Indian university is provided. Empirical data along with a brief reflection on the learners' voices are used in the study to examine the efficacy of the structure, administration and evaluation practices of this new course. The study thus opens up possibilities of initiating a discourse around the mode in which English language education and teaching is envisioned, formulated and implemented in undergraduate programs across urban India. 2020 ELE Publishing. All rights reserved. -
Mechanical Properties of FSW Joints Magnesium Alloy at Different Rotational Speeds
Magnesium (Mg) has become a focus in the transportation industry due to its potential in reducing fuel consumption and gas emissions while improving recyclability. Mg alloys are also known for their low neutron absorption, good resistant of carbon dioxide as well as thermal conductivity which makes them suitable for use in industrial equipment for nuclear energy. there has been an increasing interest in the research and development of Mg alloys. These are the lightest of all metallic structural materials and are approximately 33% lighter than aluminium (Al) and 75% lighter than ferrous (Fe) alloys and have excellent specific mechanical properties. In this work, FSW of AZ31B Alloy was examined at the various rotational speeds of 900 -1440 rpm, with fixed welding speed of 40mm/min and 2 tool tilt angle using an HSS tool. The mechanical properties were compared for the different rotational speeds. The quality of FSW joints is dependent on input value of heat and material flow rate, which are prejudiced by process parameters., higher rotation speeds may cause abnormal stirring, resulting in a tunnel defect at the weld nugget due to increased strain rate and turbulence. 2024 E3S Web of Conferences