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A Modified Seven-Level Inverter with Inverted Sine Wave Carrier for PWM Control
The conventional multilevel inverter necessitates more active switching devices and high dc-link voltages. To minimalize the employment of switching devices and dc-link voltages, a novel topology has been proposed. In this paper, a novel minimum switch multilevel inverter is established using six switches and two dc-link voltages in the proportion of 1: 2. In addition, the proposed topology is proficient in making seven-level voltages by appropriate gate signals. The PWM signals were produced using several inverted sine carriers and a single trapezoidal reference. When compared to other existing inverters, this configuration needs fewer components, as well as fewer gate drives. Furthermore, this module can generate a negative level without the use of a supplementary circuit such as an H-Bridge. As a result, overall cost and complexity are greatly reduced. The proposed minimum switch multilevel inverter operation is validated through simulations followed by experimental results of a prototype. 2022 Arun Vijayakumar et al. -
LENN: Laplacian Probability Based Extended Nearest Neighbor Classification Algorithm for Web Page Retrieval
Web page prediction is the area of interest that enables to tackle the problem of dealing with the massive collection of the web pages, mainly, in retrieving the highly relevant web pages. The hectic challenge of the web page prediction methods relied on time-effective and cost-effective management. The problem of dealing with the issue is tackled using the efficient web page retrieval algorithm. The paper proposes a new classifier called, Laplacian probability based Extended Nearest Neighbor (LENN)that is formed through the integration of the Laplacian probability with the Extended Nearest Neighbor (ENN)classifier. The proposed LENN classifier determines the nearest web pages of the query. Accordingly, the web page retrieval is done in three important steps, such as pre-processing, feature indexing and web page retrieval. The key words are stored in the database for performing the feature match such that the highly relevant web page is retrieved based on the maximum value of the score. The experimentation using five benchmarks prove that the proposed method is effective compared with the existing methods of web page retrieval. The maximum precision, recall, and F-measure of the proposed method is found to be 98%, 96.7%, and 97.3%, respectively. 2019 IEEE. -
Power law coefficient effects on buoyant heat transfer in porous trapezoidal enclosures
The investigation of steady, incompressible, laminar mixed convective fluid flow within two different types of trapezoidal enclosures filled with saturated water and study explores how the power-law index governs buoyancy-driven heat transfer in a porous trapezoidal cavity filled with non-Newtonian fluids. Unlike Newtonian fluids, non-Newtonian fluids exhibit flow behavior that directly depends on the power-law index, which characterizes their shear-dependent viscosity. We formulate the governing equations in terms of the stream function and temperature and solve them using a validated, in-house MATLAB solver. Embedding a porous matrix within a trapezoidal enclosure creates intricate interactions between convective currents and conductive resistance. By performing numerical simulations across a range of Rayleigh numbers (Ra = 102 to 2 103) and boundary conditions, we systematically assess how variations in the power-law index alter local velocity fields, temperature distributions and overall heat-transfer rates. Our results reveal that increasing the power-law index strengthens convective flow and raises the average Nusselt number, whereas decreasing the index shifts the balance toward diffusion-dominated transport. These findings offer practical guidance for enhancing thermal management in industrial systems that employ both Newtonian and non-Newtonian fluids within porous structures. The study presents new empirical correlations linking Nu, Ra and power law co-efficients offering a practical tool for engineering design. Unlike previous works that focused primarily on Newtonian fluids or simplified geometries, this work provides a detailed analysis of non-Newtonian effects in realistic porous enclosures. These results contribute to a deeper understanding of convective mechanisms in complex therm-ofluid systems and offer guidance for optimizing thermal performance in engineering applications. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment
Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%. 2022 Tech Science Press. All rights reserved. -
A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis
The rapidly evolving nature of cyber-attacks significantly reduces the effectiveness of conventional intrusion detection systems (IDS) that rely on static rules and signatures. This work presents an adaptive deep learning- based intrusion detection framework designed to maintain reliable performance in real-time environments affected by concept drift. The proposed approach integrates one-dimensional convolutional neural networks (1D-CNN) for local feature interaction learning with a bidirectional long short-term memory (BiLSTM) network to model sequential network traffic behavior. To address evolving attack patterns, a sliding-window-based incremental learning mechanism is employed, enabling continuous model adaptation to recent traffic characteristics. The model is trained using cross-entropy loss optimized with the Adam optimizer, while dropout regularization is applied to reduce overfitting and ensure fast convergence. To enhance transparency and analyst trust, explainable artificial intelligence techniques are incorporated, including SHAP-based feature attribution and an attention mechanism for interpreting temporal dependencies. Experimental evaluation on labeled network traffic data demonstrates stable convergence, consistent detection accuracy under changing traffic conditions, and improved robustness compared to non-adaptive baseline models. These results confirm the effectiveness and practical applicability of the proposed framework for real-time and interpretable cybersecurity intrusion detection. 2026 IEEE. -
Riding the La Poderosa Politics, Youth, and Motorcycle Diaries in Kerala
[No abstract available] -
Kunde Habba The Profane and the Sacred
[No abstract available] -
Memorialisation and Identity in Mah India: Revealing French Colonial Legacies
Mah nestled in the Mahdistrict of the Puducherry Union Territory in India, holds profound historical ties to French colonial India. Unlike the broader Indian subcontinent, which witnessed fervent anti-colonial movements against British rule leading to political decolonisation in 1947, Mahexperienced a belated political awakening, reluctantly integrating into the Indian Union in 1954. Despite the withdrawal of the French, the enduring legacy of French colonial ideology and culture continued to shape the ethos of Mah In contemporary times, a significant presence of French nationals in India, particularly in Pondicherry, Karaikal, and Mah has fostered the evolution of a unique linguistic identity known as Indian French. Within Mah landmarks such as St. Teresas Shrine, the Statue of Marianne in Tagore Park at Cherukallayi, remnants of St. George Fort, and sculptures inspired by M. Mukundans novel On the Banks of the Mayyazhi stand as tangible vestiges of the erstwhile French presence. Serving as repositories of bygone French culture, these sites emerge as dynamic arenas of memory production. Notably, Tagore Park in Mah adorned with fictional documentation through sculptures, assumes a pivotal role as a space that harmonizes memory and history, functioning as a reservoir for collective memory concerning French colonial rule. Mah deliberate urban planning reflects a nuanced approach, embodying the concept of a living testament to French colonialism rather than a conventional museum. This architectural strategy underscores the deliberate preservation and commemoration of Mah historical past. Through interviews with French nationals residing in Mah this research explores how these landmarks have become pivotal in the production of memories and the construction of identities for the French community in India and Mah Leveraging Maurice Halbwachs theoretical framework, the study unveils the intricate interplay between collective memory and present-day identity formation, shedding light on the transformation of personal memory into historical memory and its subsequent amalgamation into collective memory. With close to 50 French families residing in and around Mahstill, the study involves interviews with ten families, focusing on landmarks like St. Teresas Shrine, the Statue of Marianne, the ruins of St. George Fort, and sculptures based on one of M. Mukundans novels. So, through interviews of the French citizens of Mah this paper highlights how the cultural artefacts and popular landmarks of Mahbecome sites of memory of the French colonisation. 2024, The International Academic Forum (IAFOR). All rights reserved. -
Mahe's Memorialisation of French Colonialism
[No abstract available] -
Exploring the synergy of IIoT, AI, and data analytics in Industry 6.0
This chapter delves into the transformative intersection of artificial intelligence (AI), Industrial Internet of Things (IIoT), and data analytics within the context of emerging Industry 6.0. As industries continue to emerge towards greater connectivity and automation, the chapter delivers a comprehensive analysis of the convergence of these cutting-edge technologies in reshaping the industrial landscape. It explores the synergistic relationships among IIoT, AI, and data analytics, examining their collaborative potential to enhance efficiency, productivity, and decision-making processes. The chapter begins by offering an in-depth overview of Industry 6.0, highlighting the technological advancements and paradigm shifts that characterize this era. Subsequently, it dissects the role of IIoT as a pivotal enabler, connecting physical devices and systems to facilitate real-time data exchange. The incorporation of artificial intelligence is explored as a premeditated augmentation, empowering machines to learn, adapt, and optimize operations autonomously. Simultaneously, the chapter investigates the significance of advanced data analytics techniques in extracting actionable insights from big data, fueling informed decision-making and predictive maintenance strategies. Furthermore, the chapter delves into practical applications and case studies showcasing successful implementations of this triad in diverse industrial sectors. 2025 selection and editorial matter, C Kishor Kumar Reddy, Srinath Doss, Lavanya Pamulaparty, Kari Lippert and Ruchi Doshi; individual chapters, the contributors. -
Approaches Towards A Recommendation Engine for Life Insurance Products
Recommender engines are powerful tools in today's world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely - Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics - age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost. 2021 IEEE. -
Comparing Strategies for Post-Hoc Explanations in Machine Learning Models
Most of the machine learning models act as black boxes, and hence, the need for interpreting them is rising. There are multiple approaches to understand the outcomes of a model. But in order to be able to trust the interpretations, there is a need to have a closer look at these approaches. This project compared three such frameworksELI5, LIME and SHAP. ELI5 and LIME follow the same approach toward interpreting the outcomes of machine learning algorithms by building an explainable model in the vicinity of the datapoint that needs to be explained, whereas SHAP works with Shapley values, a game theory approach toward assigning feature attribution. LIME outputs an R-squared value along with its feature attribution reports which help in quantifying the trust one must have in those interpretations. The R-squared value for surrogate models within different machine learning models varies. SHAP trades-off accuracy with time (theoretically). Assigning SHAP values to features is a time and computationally consuming task, and hence, it might require sampling beforehand. SHAP triumphs over LIME with respect to optimization of different kinds of machine learning models as it has explainers for different types of machine learning models, and LIME has one generic explainer for all model types. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Real-Time Fire Detection Through the Analysis of Surveillance Videos
The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. 2024 IEEE. -
A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics
With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one's own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers. 2024 World Scientific Publishing Co. -
Influence of annealing on the morphological, structural and electrochemical properties of Co3O4 spinel electrodes
Effectual use of energy requires the conversion and storage device with great ability. In this research, Co3O4 nanoparticles are achieved via facile and low-cost reflux method. The consequence of annealing treatment on morphological, structural, and electrochemical behaviors of produced Co3O4 (350, 550, 750 and 950 C) nanoparticles are investigated. XRD analysis exposes the formation of cubic Co3O4 spinel above 300 C annealing temperature. SEM and EDX study demonstrate that the morphology of Co3O4 nanoparticles changes with different annealing temperatures. The electrochemical performance of prepared Co3O4 (350950 C) nanoparticles was determined via charge-discharge experiment, and electrochemical impedance, cyclic voltammetry studies. It exposes that the annealing treatments have an important effect on the specific capacitances. Among them, the optimized Co3O4 (950 C) electrode demonstrates the best capacitive behaviors in the three-electrode cell, which exhibitions the best capacitance value of 1388 Fg?1 at 5 mVs?1 and outstanding cycling capability of 97.2 % capacitance even after 5000 cycles. The asymmetric supercapacitor device assembled by Co3O4 (950 C) displays a capacitance value of 519.3 Fg?1 for 5 mVs?1 and long reversible capacity (92.7 % capacitance retains after 5000 cycles) and a high-power density (26.7 W h Kg?1). These outcomes exposed that the Co3O4 (950 C) nanoparticles could be a perfect candidate for eminent electrochemical application as electrode materials. These results state that Co3O4 nanoparticles are a multipurpose material and thus can be applied in numerous applications namely gas sensors, fuel cells, solar cells, electrochemical sensors, and photocatalysis. 2023 Elsevier Ltd -
Effects of nitrogen, sulphur, and temperature treatments on the spectral, structural, and electrochemical characteristics of graphene oxide for energy storage applications
The structural and surface modifications have been studied on the hydrothermally Nitrogen (N) and Sulphur (S) doped and thermally reduced at 350 C nitrogen-doped, nitrogen-sulfur-doped graphene oxides. Raman spectra confirmed the reduction of graphene oxides by shifts in position and intensity variations of the D and G bands. EDX and mapping images revealed the carbon-oxygen ratio as well as the doping of nitrogen and sulphur into two-dimensional graphene oxide. The electrochemical properties of undoped and doped graphene oxides were investigated using a three-electrode system using a 1 M KOH electrolyte. It shows how doping, and reduction improve current conduction in graphene oxides. The specific capacitance of N,S-rGO after being synthesized and reduced at 350C was 930 Fg?1 and 1059 Fg?1, respectively, according to cyclic voltammetry results. The N-rGO specific capacitance was found to be similar, with 850 Fg?1 and 891 Fg?1, respectively, for the as prepared and reduced at 350C. The charge-discharge analysis, cycle stability, and impedances for the applied frequency ranges of undoped and doped graphene oxides for energy storage applications have all been estimated and discussed. 2023 -
N-rGO/NiCo2O4 nanocomposite for high performance supercapacitor applications
Spinel structured transition metals oxide GO/NiCo2O4 nanocomposites and nitrogen doped N-rGO/NiCo2O4 nanocomposites were developed. Powder X-ray diffraction investigations confirmed the structure. The bonding vibrations of the produced nanocomposites were confirmed using infrared and Raman spectroscopy. EDX analysis was used to determine the composition and element weights of the nanocomposites. The electrochemical properties of the nanomaterials were measured using 1M KOH electrolyte. At 5mVs?1 scan rates, cyclic voltammetry revealed a specific capacitance (Csp) of 1078.2 Fg?1 for N-rGO/NiCo2O4. The bare and nanocomposites of NiCo2O4, GO/NiCo2O4, and N-rGO/NiCo2O4 specific capacitance, charge-discharge capability, and cyclic stability were investigated. Energy density and power density of the N-rGO/NiCo2O4 nanocomposite were estimated to be 20.4 Wh kg?1 and 1300W kg?1, respectively. N-rGO//N-rGO/NiCo2O4 asymmetric supercapacitor device with Ed of 14.9 Wh kg?1 and Pd of 3500W kg?1 was fabricated. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Development and effectveness of a screening measure and a computerized cognitive remediation program for children with reading and arithmetic difficulties
Reading and arithmetic skills are considered as the foundation skills necessary for educational and vocational success. Research has outlined the important role of specific cognitive functions for efficient reading, comprehension of text and arithmetic processes. Learning difficulties have been shown to have a tremendous impact on later life, both in academics and social relationships. It therefore becomes newlineimperative that we identify, as early as possible, children at risk for learning newlinedifficulties and attempt to assess the relevant cognitive skills and plan and execute intervention programs to improve the efficacy of these cognitive skills. This present study has attempted to do this, keeping the Right to Intervention (RTI) model as the rationale and has focussed on designing an assessment tool along with a remediation program based on the cognitive viewpoint.The two objectives of this research were: development of a screening measure newlinefor identification of children with reading and arithmetic difficulties and development newlineof a computer based cognitive intervention program for improving reading and newlinearithmetic skills. The research was conducted in two stages. Stage one involved the newlinecompiling of the screening measure which consisted of three domains: cognitive newlineabilities, reading comprehension and arithmetic ability. The assessment measure was administered on 1091, third, fourth and fifth grade children from English medium newlineschools in South Bangalore, following the ICSE syllabus. The data obtained was newlinesubjected to item analyses and the final screening tool - Arithmetic and Reading Test newline(ART) - was developed, which comprised of tests for reading comprehension, arithmetic ability and cognitive functions, i.e., attention and concentration, visual newlineperception, visuo-spatial ability, processing and working memory. Psychometric properties were established and the ART was found to be reliable and valid. Test-retest reliability of the ART was 0.76.