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ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
The navigation of an autonomous vehicle depends mostly on the integration of multi-sensor data from sources such as LiDAR, GPS, radar, and cameras. Issues like sensor noise, data asynchrony, and fusion inaccuracies hamper reliable real-time decision-making. This paper proposes an optimized multi-sensor data fusion framework integrating big data analytics with modern filtering techniques to increase navigation accuracy and system robustness. The proposed model integrates Kalman Filter (KF), Extended Kalman Filter (EKF), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic state estimation and adaptive noise accommodation. In addition, sensor reliability and position tracking are enhanced via Bayesian data fusion and Particle Filter. Simulation results show that the proposed technique is evidently superior to existing models in accuracy (1.5 RMSE), convergence time (0.98s), and latency (50 ms). The fusion system enhances stability and responsiveness in autonomous navigation and offers an intelligent transportation framework that can be deployed efficiently at a real-time scale. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Android security issues and solutions
Android operating system uses the permission-based model which allows Android applications to access user information, system information, device information and external resources of Smartphone. The developer needs to declare the permissions for the Android application. The user needs to accept these permissions for successful installation of an Android application. These permissions are declarations. At the time of installation, if the permissions are allowed by the user, the app can access resources and information anytime. It need not re-request for permissions again. Android OS is susceptible to various security attacks due to its weakness in security. This paper tells about the misuse of app permissions using Shared User ID, how two-factor authentications fail due to inappropriate and improper usage of app permissions using spyware, data theft in Android applications, security breaches or attacks in Android and analysis of Android, iOS and Windows operating system regarding its security. 2017 IEEE. -
Anchored ferrocene based heterogeneous electrocatalyst for the synthesis of benzimidazoles /
Electrochimica Acta, Vol.435, ISSN No: 0013-4686.
A facile and sustainable electrochemical synthetic strategy for phenyl <a href="https://www.sciencedirect.com/topics/chemistry/benzimidazole" title="Learn more about benzimidazoles from ScienceDirect's AI-generated Topic Pages" class="topic-link">benzimidazoles</a> has been developed using a ferrocene-based <a href="https://www.sciencedirect.com/topics/chemistry/electrocatalyst" title="Learn more about electrocatalyst from ScienceDirect's AI-generated Topic Pages" class="topic-link">electrocatalyst</a> anchored on Toray carbon paper (TCP) coated with conducting polymeric film. The developed electrode was used for the electrochemical dehydrogenative <a href="https://www.sciencedirect.com/topics/chemistry/cyclization-reaction" title="Learn more about cyclization reaction from ScienceDirect's AI-generated Topic Pages" class="topic-link">cyclization reaction</a> of </span><em>o</em><span>-phenylene <a href="https://www.sciencedirect.com/topics/chemistry/diamine" title="Learn more about diamine from ScienceDirect's AI-generated Topic Pages" class="topic-link">diamine</a> and <a href="https://www.sciencedirect.com/topics/chemistry/benzaldehyde" title="Learn more about benzaldehyde from ScienceDirect's AI-generated Topic Pages" class="topic-link">benzaldehyde</a> using lithium perchlorate/acetonitrile as electrolyte. The surface characteristic properties of the developed electrode were characterized by <a href="https://www.sciencedirect.com/topics/chemistry/field-emission-scanning-electron-microscopy" title="Learn more about FESEM from ScienceDirect's AI-generated Topic Pages" class="topic-link">FESEM</a>, Optical profilometer and X-ray photoelectron spectroscopy. </span> -
Anchored ferrocene based heterogeneous electrocatalyst for the synthesis of benzimidazoles
A facile and sustainable electrochemical synthetic strategy for phenyl benzimidazoles has been developed using a ferrocene-based electrocatalyst anchored on Toray carbon paper (TCP) coated with conducting polymeric film. The developed electrode was used for the electrochemical dehydrogenative cyclization reaction of o-phenylene diamine and benzaldehyde using lithium perchlorate/acetonitrile as electrolyte. The surface characteristic properties of the developed electrode were characterized by FESEM, Optical profilometer and X-ray photoelectron spectroscopy. Electron transfer mechanism of the anchored ferrocene-based electrocatalyst was thoroughly studied. To determine the efficacy of the catalyst, the electron transfer coefficient (0.5) and apparent rate constant 41.4 s?1 were determined. The cyclic voltammetry studies reveal that the electrochemical oxidation peak for the synthesis of benzimidazole occurs at 0.48 V. The formation of the product was confirmed by Gas chromatography and Nuclear Magnetic Resonance spectroscopy. A comparison chart is presented for the green metrics and sustainability of the present strategy with other electrochemical approach. 2022 Elsevier Ltd -
Analyzing Wholesale Trade Volume in Uzbekistan: A Data-Driven Study of Internal Trade Dynamics
Wholesale trade as a segment of internal trade possesses a great potential for shaping supply chain management, and market conditions in Uzbekistan. This paper discusses monthly changes of the wholesale trade volume regarding its main issues, seasonal fluctuations, and regional diversification using the SDMX dataset. Using timeseries analysis, trend decomposition, and correlation modeling, this paper aims at determining the effects of the economic policy, consumer demand factors, and trade restrictions in the wholesale trade sector in Uzbekistan. It also reveals seasonality in the trade, trade heterogeneity across regions, and impact of a shock in the market, which is very relevant and useful for policymakers and key players in the trade business. 2025 IEEE. -
Analyzing Uzbekistan's Export Trends: A Data-Driven Study on Annual Export Volume by Country
Uzbekistan's export sector plays a crucial role in economic development, contributing to GDP growth and international trade partnerships. This study analyzes annual export volume by country using the SDMX dataset, highlighting key trends, regional trading partners, and sectoral contributions. The research employs time-series analysis, trade flow modeling, and export diversification measures to provide insights into Uzbekistan's evolving trade structure. The results indicate shifting export dynamics influenced by geopolitical factors, regional economic policies, and trade agreements. Policy recommendations focus on enhancing market diversification, improving export efficiency, and fostering trade competitiveness. 2025 IEEE. -
Analyzing the Vulnerability of Cyber-Attacks in IoMT Devices Using Generative AI
The rapid expansion of Internet of Medical Things (IoMT) gadgets has revolutionized healthcare delivery allowing for real-time monitoring and better patient outcomes. However, this growth has also brought about cybersecurity risks making IoMT devices targets, for cyber attacks. This study delves into the connection between Generative AI and IoMT security emphasizing how advanced AI methods can be used to pinpoint, assess, and address vulnerabilities in these devices. We explore uses of Generative AI, such as simulating cyber-attacks and creating models for future threats to strengthen the resilience of IoMT systems against emerging dangers. By examining existing literature and case studies we showcase the effectiveness of AI-driven strategies in bolstering healthcare cybersecurity. Our results highlight the need for innovation in security measures to protect medical information and uphold the reliability of healthcare services. Ultimately this research contributes to discussions on enhancing cybersecurity protocols in the healthcare industry by advocating for integrating Generative AI as a tool, in combating cyber threats targeting IoMT devices. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Analyzing the Virtual Reality Experiential Dimensions at the Game Centers of Tourist Destinations
Virtual Reality (VR) games have attracted the attention of customers lately since they have been offering the most immersive experience through amusement park rides such as VR roller coasters and VR games related to adventure, thrill, scare, etc. Bangalore being a gem of the tourist destination and an IT hub was chosen for the study as it has the greatest potential of offering various VR experiences to the customers. The top 6 of Bangalore's VR game centers were selected based on the popularity and review count from Trip Advisor and Google reviews websites. Analyzing user-generated content has become an intriguing part of business research to find valuable marketing insights for better decisionmaking. The empirical findings show that the majority of the customers are extremely satisfied with the VR experiences and illusion emerges to be the major influencing factors for experiential satisfaction and customers are ready to spend for VR when the VR experiential dimensions meet the expected standards. 2024, Journal of Toursm & Development. All rights reserved. -
Analyzing the therapeutic significance of Strelitzia reginae Banks: Exploring its physico-chemical properties, elemental makeup, and antimicrobial activity
Plants constituting biologically active molecules with curative value have overtime showed advantage as subject of researches. Strelitzia reginae (Bird of Paradise) is a member of the Strelitziaceae family. Several South African tribes used plant parts to treat the venereal diseases and inflamed glands. The study aimed to investigate therapeutic potential of leaf and root extracts of S. reginae by assessing the physico-chemical properties, elemental analysis. Elemental analysis was carried out by Atomic Absorption Spectrometry (AAS) method, quantitative phytochemical analysis was carried out using, Gas Chromatography and Mass Spectrometry (GC-MS) analysis. The leaf and root of S. reginae were extracted using soxhlet technique of extraction and was further concentrated with a rotary evaporator. Standard protocols assessed the plants elemental compounds, physico-chemical properties, qualitative and quantitative phytochemicals, GC-MS analysis, antioxidant activity using DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay, phosphomolybdate assay, ferric reducing power assay (FRAP), metal chelating assay, and antimicrobial potential by well diffusion test. The results of AAS exhibited that the leaf and root contain more calcium and less of cadmium content. Preliminary phytoconstituents showed the presence of medicinally important alkaloids, anthraquinones, tannins, carbohydrates, flavonoids, saponins, phenols, proteins, and amino acids. The quantitative phytochemical analysis revealed that the leaf has higher total phenolic, flavonoid, chlorophyll, carbohydrates, protein, and proline contents than root. GC-MS analysis verifies the existence of bioactive components like squalene, hexatriacontane, phytol, hexacosane, heptacosane, and octacosane. DPPH, phosphomolybdate assay, FRAP and metal chelating antioxidant analysis revealed excellent activity in leaf and in root sample. As various South African tribes employed plant parts to treat sexual diseases and swollen glands, the antimicrobial property was investigated for the first time using a well-diffusion approach, and both plant parts revealed significant antibacterial and antifungal efficacy against recognized strains. The current study showed S. reginaes therapeutic potential and asked for more pharmacological and biological research to boost the importance of the worlds unevaluated herbal plants. 2024, Indian journals. All rights reserved. -
Analyzing the synchronization and causality between Indias financial and business cycle: empirical evidence and policy insights
Purpose This research aims to develop an aggregate financial cycle for India and understand its interrelationship with the business cycle. To study this relationship, the research focuses on examining the level of synchronization, comovement and leadlag relationship between the aggregate financial cycle and the business cycle of India. Design/methodology/approach The study uses principal component analysis and wavelet transform analysis to develop the aggregate financial cycle and understand its time-frequency characteristics, respectively. Then the study undertakes a three-step econometric analysis to measure the various aspects of the relationship between the financial and business cycles. Findings The study found that the aggregate financial cycle and the Indian business cycle have long-term equilibrating relationships. The comovement and the degree of synchronization between the two cycles are moderate, which shows that the relationship between them is relatively dynamic. Further, the leadlag relationship indicated that the financial cycle often leads the business cycle and not vice versa. Originality/value The research stands out as one of the few works to capture multiple dimensions of the financial market into a single aggregate financial cycle to present a broader picture of an emerging market setting, such as India. This study adds to the literature by systematically investigating the relationship between financial and business cycles over the short-, medium- and long-term horizons. Emerald Publishing Limited -
Analyzing the Role of LIME and SHAP in Explainable DoS Attack Detection for IoT Systems
Explainable Artificial Intelligence (XAI) based tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are extensively used in various detection and prediction approaches. These tools extract feature importance from the datasets and explain the contribution of the features (feature importance) towards detection /prediction output both locally and globally. In the current study a performance analysis is represented on the behaviour of LIME and SHAP explainability towards Denial-of-Service Attack detection in Internet of Things. There are numerous Black-box models including Machine Learning which show high detection accuracies in such case but the output is not interpretable by the security analyst most of the time. this drawback is overcome by introducing LIME and SHAP interpretability to the output of BlackBox model by analysing feature importance of the attack dataset towards detection accuracy. However, LIME and SHAPE has different behaviour towards model-interpretability. SHAP is powerful in global explanation where LIME works efficiently on local interpretation. We have shown that these two different tools perform on same detection accuracies of DoS attack using Machine learning model. A random forest classifier is first selected with high detection accuracy on a simulated DoS attack dataset and at the output SHAP and LIME are executed for achieving both local and global explainability. The comparison shows how SHAP and LIME show strength and weakness in explaining model's behaviour both locally and globally. 2025 IEEE. -
Analyzing the Prospects of Blockchain in Healthcare Industry
Deployment of secured healthcare information is a major challenge in a web-based environment. eHealth services are subjected to same security threats as other services. The purpose of blockchain is to provide a structure and security to the organization data. Healthcare data deals with confidential information. The medical records can be well organized and empower their propagation in a secured manner through the usage of blockchain technology. The study throws light on providing security of health services through blockchain technology. The authors have analyzed the various aspects of role of blockchain in healthcare through an extensive literature review. The application of blockchain in COVID-19 has also been analyzed and discussed in the study. Further application of blockchain in Indian healthcare has been highlighted in the paper. The study provides suggestions for strengthening the healthcare system by blending machine learning, artificial intelligence, big data, and IoT with blockchain. 2022 Shilpa Srivastava et al. -
Analyzing the Performance of Conformable and Non-Conformable Patch Antennas
This paper presents a performance analysis between a conventional triangular shaped patch antenna and a future reconfigurable patch antenna. There are different materials with different electronic properties for the simulation of triangular shaped patch antenna. All the materials for the triangular patch antenna are simulated using FEKO tool. Materials selected for triangular patch antenna are Copper, Single-wall Carbon Nano-tube (SCNT), Multiple-wall Carbon Nano-tube (MCNT) and Graphene. For the futuristic antennas, cotton fabric based reconfigurable patch antenna is also analyzed and compared with triangular shaped patch antenna. Graphene based triangular patch antenna has been analyzed best out of other materials. Reconfigurable cotton fabric-based patch antenna provides better bandwidth and results are validated through simulation and experimental setup. 2024 IEEE. -
Analyzing the Performance of Canny Edge Detection on Interpolated Images
Surveillance cameras are extensively used nowadays in many commercial and domestic places to monitor theft, intrusion and other illegal activities. Typically, the cameras are placed at a high position to monitor a large area. Therefore, the captured images include background area in addition to the target objects. Under such situation, the image can be zoomed to focus on the target objects using various interpolation techniques. For further processing of the image, many techniques like edge detection, image sampling and image thresholding etc. are available. Considering edge detection to be a basic step for many application such as Object detection, Object recognition etc, in this work, we analyze the performance of the Canny Edge Detection algorithm on images interpolated using Nearest Neighbour, Bilinear and Bicubic interpolation methods. Canny Edge Detection is applied to the input image and the resultant image is saved for later comparison. The same image is upscaled using interpolation and the Canny Edge Detection algorithm is used on this upscaled image. This image is then resized to the original image size. Both the images are compared to check for their similarity using the Structural Similarity Index Method (SSIM). 2019 IEEE. -
Analyzing the Nexus Between Corporate Governance, Green Finance and the Competition Laws
Green Finance plays an important role in enhancing Environmental and Social Governance (ESG) by aligning the Financial Regulations with the Sustainable Development Goals. It is a regulatory mechanism to mitigate environmental and social risks and rather than a critique to the financial markets. The corporate sector contributes to this through mechanisms like institutional investments, corporate social responsibility, preventing cartelization in ESG washing, establishing the ESG teams for Social, Economic and Environmental Security and more. This chapter explores a triangular nexus between corporate governance, green finance, and competition law, particularly focusing on the Indian regulatory and policy landscape. It examines how cartelization and other anti-competitive practices can undermine ESG objectives, creating an ESG Backlash. This discussion is framed through a comparative perspective referencing global developments and contrasting them with Indias evolving frameworks like the SEBI guidelines, CSR mandates and competition law provisions. The chapter further identifies the gaps and ambiguities in existing Indian laws, particularly in emerging domains such as the ESG team governance within corporations and underscores the need for more robust legal and institutional frameworks. By integrating regulatory analysis with case-based insights, it seeks to provide practical policy recommendations for improving corporate fiscal management of climate and environmental challenges. 2026 selection and editorial matter, Kirti Sood, Vikas Sharma, Andreia de Bem Machado; individual chapters, the contributors. -
Analyzing the Market Dynamics of Electrical Appliances with a Special Emphasis on Sustainable Electric Energy
This study looks into the market dynamics of electrical appliances with a special emphasis on sustainable electric energy. The research aims to understand how factors such as technological advancements, consumer behavior, and regulatory policies influence the electrical appliances market. By examining the trends and challenges within this sector, the study highlights the growing importance of sustainability in product development and consumer choices. The main areas of focus include the adoption of energy-efficient technologies, the impact of rising household incomes on appliance usage, and the role of government policies and initiatives in promoting sustainable energy consumption. The findings of the study would provide insights into how the industry can align its practices with environmental goals while meeting the evolving needs of consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analyzing the interactions among delay factors in construction projects: A multi criteria decision analysis
The construction industry is a crucial sector that drives economic growth and facilitates socio-economic development. However, construction projects often get delayed due to multiple controllable and uncontrollable circumstances. In this scenario, the construction industry is striving for potential solutions to resolve project delays. Thus, the present study objectives to analyze the delay factors that affect the timely accomplishment of construction projects in the context of emerging economies. The study adopts a mixed methodology comprising of Delphi, Total Interpretive Structural Modelling (TISM) and Matrice d' Impacts Croises Multiplication Applique a Classement (MICMAC) method to model the identified delay factors. A Delphi analysis was conducted to finalize the most crucial delay factors to the on-time completion of building projects. The causal relationships and expert interpretations for each identified delay factor were then determined using multi-criteria decision analysis, TISM and MICMAC analysis. The study results highlight that lack of knowledge of newer construction methodologies and lack of project monitoring tools and techniques are positioned at the bottom level of model, which suggests that this delay factor influences others. The study results will help managers resolve the issues of project delays by selecting the most suitable approach. The findings from the study suggest adopting advanced technologies for effective communication, use of analytical tools for resource allocation and waste-scrapping approaches for eliminating delays in construction projects. The Author(s) 2023. -
Analyzing the Inter-relationships of Business Recovery Challenges in the Manufacturing Industry: Implications for Post-pandemic Supply Chain Resilience
The COVID-19 pandemic brought about a rapid change in the global business environment, leading to increased risks of supply and demand disruptions. As society and the industry continue to acclimate to the new normal, the contributions of the manufacturing industry are critical in the recovery process. However, the existing literature lacks a framework to analyze the manufacturing sectors challenges during the recovery to enhance supply chain resilience (SCR). To address this gap, this study develops a framework for business recovery, especially in the manufacturing sector. A broad literature examination and expert survey were conducted to identify the critical potential business recovery challenges. Further, the interplay of business recovery challenges was analyzed using mixed methodologies such as total interpretive structure model and the cross-impact matrix multiplication applied to classification (MICMAC) to foster a framework that can assist the manufacturing industry in improving SCR. The study found that challenges like lack of flexible policies for handling disruptions and lack of management support toward building resilience have the highest driving power impeding business recovery. Other challenges, such as lack of reconfiguring production lines, lack of product competencies to meet disturbances, and less adoption of robust technologies are also identified as major challenges. The implications of the study offer valuable insights into global manufacturing industries. It also has significant propositions for the Pacific region. The Pacific region faces unique challenges, including geographic isolation, resource dependency, diverse economies, climate vulnerabilities, and complex trade relationships. The suggested frameworks adaptability and applicability to these regional characteristics enable businesses and policymakers in the Pacific to better understand and address the specific dynamics of post-pandemic recovery, ultimately contributing to enhanced SCR tailored to the regions needs. The study enriches the existing SCR literature by analyzing inter-relationships between business recovery challenges in the manufacturing industrys post-pandemic context. The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2024. -
Analyzing the Impact of Green HRM Practices on Employee Performance
Green practices are gaining interest everywhere and the Human Resource domain is no different. Green Human Resource Management (Green HRM) practices have surfaced as a critical strategic component, with the intensification of worldwide focus on environmental sustainability, particularly in sectors such as insurance where the operational ecological impacts are significant. The association between the execution of Green HRM and enhancements in employee performance is what this study aims to explore, specifically within insurance companies in the Delhi NCR region. To ascertain the results, data collection has been carried out through surveys and analyzed using statistical techniques such as reliability statistics and descriptive statistics. A total of 516 samples were collected, of which 467 samples have been analyzed to conclude the results. The findings reveal that Green HRM practices positively impact employee performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analyzing the Impact of Blockchain Technology on Financial Transactions and Accounting Practices in Global Trade
Financial transactions and accounting practises in global trade are transformed using blockchain technology with enhanced security efficiency and transparency. The study examines the role of Blockchain in real time financial reports smart contracts and cross border transactions by highlighting the potentiality to ensure regulatory compliance streamlined reconciliation process and fraudulence reduction. Decentralisation of financial data using blockchain enable record keeping a stamper proof and also minimises the transaction cost and the intermediaries and to accelerate the settlement times. The study explores the challenges such as integration regulatory uncertainties and scalability in the existing financial system. The study involves trade finance platforms and multinational corporations in reshaping the financial management and accounting standards using blockchain adoption in the global trade. The study employs mixed method approach for quantitative and qualitative analysis based on the ability of the blockchain to enhance the financial transactions in global trade. 2025 IEEE.