Tuijin Jishu/Journal of Propulsion Technology
Tuijin Jishu, which translates to "Propulsion Technology" in English, is a prestigious and cutting-edge academic journal dedicated to advancing the field of propulsion engineering and technology. Our mission is to serve as a platform for researchers, engineers, and enthusiasts alike to share their knowledge, discoveries, and innovations in the propulsion domain. At Tuijin Jishu, we believe that propulsion technology is at the forefront of modern scientific and engineering achievements, driving humanity forward in space exploration, aviation, transportation, and beyond. As such, we are committed to promoting the dissemination of high-quality, peer-reviewed research that pushes the boundaries of propulsion systems, fuels, and related technologies. Tuijin Jishu is a peer-reviewed open-access journal, ensuring that all published articles undergo rigorous scrutiny by expert reviewers in the field. We strive to maintain the highest standards of integrity and scientific accuracy, upholding the trust and credibility of our readers and contributors. Our journal covers a broad spectrum of propulsion-related topics, including but not limited to: Rocket Propulsion: Advancements in chemical propulsion, hybrid systems, electric propulsion, and novel propulsion concepts. Aerospace Propulsion: Cutting-edge research on aircraft engines, propulsion systems, and aero-thermodynamics. Space Exploration: Innovations in space propulsion, interplanetary travel, and propulsion challenges in long-duration missions. Green Propulsion: Sustainable and eco-friendly propulsion technologies, biofuels, and reducing environmental impact. Advanced Materials: Research on materials for propulsion systems, thermal management, and structural integrity. Computational Fluid Dynamics (CFD): Modeling and simulation techniques to optimize propulsion performance. Propulsion Control Systems: Developments in autonomous and intelligent control of propulsion systems. Why Tuijin Jishu? Expertise: Our editorial board comprises esteemed professionals and researchers from diverse propulsion-related disciplines, ensuring a balanced and multidisciplinary approach to propulsion technology. Global Community: Tuijin Jishu fosters collaboration among propulsion enthusiasts worldwide, breaking down geographical barriers and promoting international cooperation. Innovation: We celebrate innovation and originality, encouraging authors to present groundbreaking research that contributes to the advancement of propulsion technology. Open Access: Tuijin Jishu follows the open-access model, making all published articles freely available to the global scientific community, accelerating the exchange of knowledge. Ethics and Integrity: We uphold the highest ethical standards in academic publishing, ensuring transparency, fairness, and confidentiality throughout the review process. Become a Contributor: Tuijin Jishu welcomes contributions from researchers, academics, and practitioners involved in propulsion technology. If you have exciting research to share or wish to stay updated with the latest developments in the field, subscribe to our journal or consider submitting your work to us.
Fuzzy Stability Analysis of a Quartic- Additive Mixed TypeFunctional Equation
In this research proposal, we exhibit the generalized Hyers-Ulam-Rassias stability of a quartic- additve mixed type functional equation in fuzzy normed space using the popular Hyers direct and fixed point methods.
An NLP-Based Approach for Automated Task Identification in Unstructured Chat Conversations
The task of identifying relevant actions from short workplace messages is difficult because of the lack of contextual information, use of informal language, and code-mixing. Although transformer models have demonstrated remarkable success in minimal-context settings, their performance in such settings has not been adequately explored. This paper presents the application of binary task identification in a single-utterance classification paradigm, where messages are assessed independently without any conversational context. Three different methods are employed for the task: a traditional lexical approach using TF-IDF with logistic regression, a multilingual transformer model with XLM-RoBERTa-base, and an optimized transformer model. Empirical evaluation on 1,190 real-world workplace messages with class imbalance and Hinglish code-mixing demonstrates that lexical approaches are robust baselines, and optimized transformer models perform best. The results show that increased model complexity does not necessarily translate to improved performance and the need for empirical analysis in designing task identification systems for minimal-context workplace communication
Comparison of Part and Full load Emissions of CI and HCCI Engine with Exhaust Gas Recirculation
The present study is experimentation, not only on CI engine but also conducting experiment on CI engine running on HCCI mode operation without using exhaust gas recirculation and with using exhaust gas recirculation and comparing the emissions of both such as the Oxide of Nitrogen (NOX), Oxides of carbon (CO), carbon dioxide (CO2), Un-burnt hydro-carbons (HC)etc., These values are measured and compared under various conditions and plotted for easy understanding of effect of EGR on engine emissions. This paper focused on part load and full load emission comparisons for CI and HCCI mode operations with and without using exhaust gas recirculation. So separate experiments conducted and readings are recorded.
Optimization of Resistance Spot Welding Parameters for HSLA 340 Steel Using Response Surface Methodology and Finite Element Simulation
Resistance spot welding (RSW) is used widely in the manufacturing of automotive body-in-white steels, in which high strength low alloy materials (HSLA) require the welding properties to be effectively established for integrity and reliability of joints. This study investigated the resistance spot welding process of HSLA 340 automotive steel sheets with dimensions of 100 × 25 × 3 mm by combined experimental, statistical and numerical approaches. To obtain a perfect welding current ratio, weld time and electrode force were identified as process parameters and optimized for RSM with a central composite design. Analysis of variance (ANOVA) indicated that welding current was the most important variable affecting the quality of the welds, then weld time and electrode force, and an interaction effect of process parameters was also statistically significant. Ideal welding parameters were established as 8.2 kA welding current, 13 cycles (0.26 s) weld time, and 3.2 kN electrode force. Due to the predicted dimensions, in particular the weld nugget diameter and maximum temperature and residual stress levels predicted in these scenarios were 6.25 mm, 1480 °C and 310 MPa. The proposed coupled electro-thermal-mechanical finite element model was synthesized in ANSYS to simulate the growth of the nuggets, the temperature distribution and the residual stress evolution during welding. The simulated findings exhibited significant agreement with RSM predictions, with deviations less than 3%. The experimental-numerical combined framework is proposed in the present work and it proves to be an efficient method to develop better resistance spot welding parameters of HSLA 340 steel as well as to enhance weld quality and process reliability in automotive applications.
Resistance Spot Welding, HSLA 340 Steel, Response Surface Methodology, Weld Nugget Formation
A Robust and Reproducible Machine Learning Pipeline for Diabetes Prediction Using Feature Engineering and Ensemble Learning
Diabetes mellitus remains a major global health challenge, requiring accurate and early prediction to reduce the risk of severe complications and improve patient outcomes. Although machine learning techniques have been widely applied to diabetes prediction, existing studies often address key challenges such as class imbalance, feature representation, and model optimization in isolation, leading to suboptimal and non-reproducible predictive performance. This study addresses this gap by proposing a comprehensive and integrated machine learning pipeline for diabetes prediction using the Pima Indians Diabetes Dataset. The proposed framework systematically combines data preprocessing, feature engineering, dimensionality reduction, class imbalance handling, and ensemble learning within a unified pipeline. Missing values are handled using median imputation, while outliers are treated through interquartile range (IQR)-based winsorization. Three domain-informed features Glucose_per_BMI, Age_BMI, and HighPreg are introduced to capture nonlinear relationships, followed by feature selection and Principal Component Analysis (PCA). To address class imbalance, SMOTE-Tomek resampling is applied, improving minority class representation. Multiple models, including Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LightGBM, and a stacked ensemble, are evaluated using cross-validation and a hold-out test set. Experimental results demonstrate that the proposed pipeline significantly improves predictive performance, with accuracy increasing from 74.03% to 82.11%, F1-score from 53.33% to 71.11%, and balanced accuracy from 0.6893 to 0.7780. Notably, recall improved substantially across models, enhancing sensitivity to diabetic cases. Additionally, recall improved substantially, with Logistic Regression increasing from 0.6267 to 0.8000 and SVM from 0.5467 to 0.7467. LightGBM and the stacked ensemble achieved the best overall performance. These findings highlight the effectiveness of integrating preprocessing, feature engineering, and ensemble learning into a unified framework for robust and reliable diabetes prediction.
Diabetes Prediction, Machine Learning Pipeline, Feature Engineering, Ensemble learning
Enhancing Adversarial Attack Detection: Insights from Ensemble Learning, Deep Dyna-Q, and VARMAx Models
Machine learning systems are increasingly crucial for various applications but are also vulnerable to hostile attacks. Current methods are often inflexible and imprecise. This paper presents a sophisticated ensemble model that combines Deep Dyna Q Learning and VARMAx procedures with Active Machine Learning Adversarial Attack Detection frameworks. The model uses a dynamic and responsive methodology, combining data pretreatment methods like tokenization and numerical conversions. Techniques like CNN, SVM, Random Forest, XGBoost, and Logistic Regression are used to improve detection capabilities. The model incorporates uncertainty sampling and query-by-committee for adaptability to new adversarial tactics. VARMAx operations improve prediction accuracy, while Deep Dyna Q Learning anticipates attack vectors. The system's defensive mechanisms are strengthened with GridCAM++ for explainability and GAN-based sample generation. The model outperforms current approaches in precision, accuracy, recall, and AUC, and reduces detection delays
Machine Learning, Adversarial Attacks, Ensemble Methods, Deep Learning, Cybersecurity.
Intelligent Medication Dispensing System with Mobile Communication and Email Notifications
This paper presents the design and implementation of an intelligent pillbox system engineered to enhance medication adherence and patient safety. Traditional pill dispensers, while functional, often lack dynamic oversight and proactive communication capabilities, leading to medication errors and missed doses. Our proposed system integrates a smart pillbox with a Global System for Mobile Communications (GSM) module and an email notification service. The pillbox is equipped with individual compartments, each capable of detecting the presence and dispensing of medication. Upon scheduled times or missed doses, the system triggers a GSM transmission to pre-registered mobile numbers, alerting caregivers or patients. Simultaneously, it dispatches a detailed email report to designated medical professionals or family members, providing a comprehensive log of medication intake. This dual communication strategy aims to create a robust support network, reducing the burden on individuals with complex medication regimens and mitigating the risks associated with non-adherence. The system's modular design and user-friendly interface are discussed, along with potential applications in home healthcare, assisted living facilities, and remote patient monitoring. The system was designed to enhance patient compliance, provide real-time monitoring for caregivers, and promote independent living for individuals managing complex medication regimens. Preliminary trials demonstrate significant improvements in medication adherence rates and caregiver peace of mind, underscoring the potential of this technological intervention.
Skin Lesion Detection: A Survey of Recent Breakthrough
Melanoma, a highly aggressive form of skin cancer, continues to rise in prevalence worldwide. Early identification of skin lesions is crucial for enhancing patient prognosis and minimizing medical expenses. Automated detection systems have emerged as essential tools in dermatology, improving diagnostic precision and efficiency. This paper explores recent advancements in skin lesion analysis, covering both conventional machine learning techniques and state-of-the-art deep learning architectures. Additionally, it discusses existing obstacles and potential future research directions in this field.
Skin lesion detection, deep learning, machine learning, convolutional neural networks, AI in healthcare.
Leveraging Online Learning as a Vital Cog to Lifelong Education in Institutions of Higher Learning (IHL) in Manicaland Province
Online learning is now an essential part of higher education due to the quick development of technology. With a focus on Zimbabwe, this article investigated how educational institutions used online learning to encourage lifelong learning. For working professionals, conventional students and non-traditional students looking to continuously improve their skills, online learning is perfect since it provides flexibility, accessibility, and personalized learning. But gaps like infrastructure, digital literacy, and internet access still exist, especially in underdeveloped nations like Zimbabwe. This study looked at how educational institutions might promote a culture of self-directed learning, improve staff and student digital abilities, and incorporate online learning into their curricula. It also emphasized how partnerships and governmental policies encourage online learning. A pragmatism research philosophy was adopted and backed by mixed research approach in this study. Purposive and stratified sampling techniques were the key sampling techniques utilised in this study. Semi-structured questionnaires were administered to both trainers and students. In-depth interviews were carried out with senior management. The major findings were that there was limited access to internet services and lack of appropriate digital skills among staff and students. At the same time erratic power cuts made online learning not sustainable. It was also observed that the government of Zimbabwe was not quite interested in some qualifications that were totally attained through online learning. However, there were benefits like increased accessibility through online learning as well as getting quality education. One of the conclusions was that institutions of higher learning could make lifelong learning possible by removing obstacles and utilizing technology, which would support both domestic growth and international competitiveness. Blended learning was one of the major recommendations. Online platforms that would use local languages were also recommended as well as introducing low interner tariffs.
Designing A Self-Learning And Secured Intrusion Detection System (Ids) For A Distributed Iot- Architecture (Similar To Smart Homes) To Propulsion Systems Using Federated Learning And Sensor Data Analytics.
The rapid usage of Internet of Things (IoT) devices equipped with sensors involves larger amounts of data that supports for intelligent data analytical applications. However, centralizing this data raises critical privacy and security concerns. Federated Learning (FL) offers a promising decentralized approach, enabling edge devices to collaboratively train machine learning models without sharing raw data. This work proposes a federated learning framework tailored for IoT sensor networks, integrating lightweight security mechanisms to ensure data privacy and secure communication under resource constraints typical of IoT environments.
Experimental Investigation of Wear Properties of Aluminium Hybrid Composite through Pin-On Disc Method.
This research focuses on the experimental investigation of the wear behavior of Al 7075-T6 alloy reinforced with Silicon Carbide (SiC) and Zirconium Dioxide (ZrO2) particles in an equal weight ratio of 5:5, prepared using the stir casting technique. The composite samples were processed with controlled stirring to ensure uniform dispersion of the reinforcements with in the aluminum matrix.X-ray Diffraction(XRD)analysis wasper formed to confirm the presence and distribution of SiC and ZrO2 in the matrix. Wear testing was conducted using a Pin-on-Disc apparatus under various loads and sliding speeds to evaluate the material's wear resistance. The results indicate a significant enhancement in wear performance compared to the unreinforced Al 7075-T6 alloy, due to the presence of hard ceramic reinforcements that effectively reduce material loss during sliding. The study concludes that incorporating SiC and ZrO2 in a 5:5 weight ratio improves the wear characteristic soft he aluminum composite, making it suitable forwear-critical applications like aerospace , aluminium casing .
Al 7075-T6, Stir Casting, Wear Behavior, Silicon Carbide (SiC), Zirconium Dioxide (ZrO2), Hybrid Composite, 5:5 Weight Ratio, XRD Analysis, Pin-on-Disc Test
Comparative Study of Green and Chemically Synthesized ZnO Nanoparticles Reinforced Waste Polyethylene Nanocomposites
The mechanical properties of Reinforced waste polyethylene nanocomposites were study. In this work, zinc oxide (ZnO) nanoparticles synthesized via chemical (CS) and green routes (GS) using garlic bark extract were incorporated into waste polyethylene to fabricate polyethylene/ZnO nanocomposites. The influence of nanoparticle synthesis route and filler loading on mechanical properties - including hardness, tensile strength, flexural strength, Young’s modulus, and elongation at break - were systematically investigated. The nanocomposites were prepared via in-situ polymerization and ZnO particles were characterized using mechanical testing, scanning electron microscopy (SEM), X-ray diffraction (XRD) and FTIR. Results revealed that chemically synthesized ZnO nanoparticles enhanced hardness and stiffness due to their higher crystallinity, while green-synthesized ZnO nanoparticles exhibited superior flexural performance owing to improved dispersion and interfacial interaction. Tensile strength and elongation at break generally decreases with increasing ZnO loading due to nanoparticle agglomeration at higher concentrations.The average crystalline size obtained 115.10 nm GS and 195.88 nm CS ZnO nanoparticle and Zn-O prominent absorption was observed between 673.18 to 437.18 cm−1 GS ZnO and 678.73 to 472.77 cm−1CS ZnO .The findings demonstrate that green synthesis provides a sustainable alternative for producing effective ZnO-reinforced polyethylene nanocomposites from plastic waste.
Analyze and Forecast the Cyber Attack Detection Process Using Machine Learning Techniques
This project, titled “Analyze and Forecast the Cyber Attack Detection Process using Machine Learning Techniques”, addresses the growing global concern of cybercrime. Cyberattacks cause significant financial losses and their frequency is steadily increasing worldwide. Identifying the criminals and understanding their strategies is essential to strengthen defense mechanisms. Machine learning techniques are applied to analyze real-world data and predict cyber-attack patterns. Five different ML techniques were compared, yielding similar accuracy in detection performance. Among them, the Support Vector Machine (SVM) linear model achieved the highest accuracy rate. The first model provided insights into the types of attacks likely to occur against victims. Logistic Regression proved effective in identifying malicious actors with high success rates. The second model compared offender and victim attributes for predictive identification. Findings show education and wealth reduce victimization risk, making the project valuable for cybercrime departments.
Deep Hybrid Framework for Early Detection of Chronic Kidney Disease Using CNN–LSTM Ensemble Models
Chronic Kidney Disease is a serious health issue that often develops slowly and shows no early signs, making timely detection very important. Regular testing and manual diagnosis can take time and may lead to errors. This study introduces a smart deep learning-based system that uses Convolutional Neural Networks , Long Short-Term Memory networks, and an Ensemble Model to predict CKD more accurately. The CNN model helps pick up useful patterns from medical images and lab test results, while the LSTM model understands time-based patterns in patient records. By mixing these two types of features, the ensemble method boosts the model’s ability to make better predictions. The system was tested on publicly available CKD datasets and outperformed regular machine learning models. The combined use of image and sequence data allows the system to learn in a more complete way, helping doctors find CKD earlier and make faster, more confident decisions. This method shows how artificial intelligence can support precision healthcare and help lower the chances of kidney failure through early and reliable prediction.
Statistical Examination on Wear Behaviour Of Za-27 Reinforced with Sic and Tib2
With increasing emphasis on environmental sustainability, there is a growing demand for materials that deliver superior performance while minimizing ecological impact. Metal Matrix Composites (MMCs) have emerged as promising alternatives to conventional materials by offering lightweight solutions that reduce energy consumption in transportation and operational applications. This study investigates the tribological behaviour of zinc–aluminium ZA-27 alloy–based hybrid MMCs aimed at developing environmentally friendly materials with enhanced mechanical performance. Silicon carbide (SiC) and titanium diboride (TiB₂) are incorporated as reinforcements to improve wear resistance, extend component service life, and reduce the frequency of replacement. The primary objective is to comprehensively evaluate the wear behaviour of ZA-27 alloy reinforced with SiC and TiB₂ and to optimize wear resistance using Taguchi statistical analysis. The experimental program includes tribological testing and microstructural examination to analyse wear mechanisms and surface morphology. Cylindrical pin and cuboidal plate specimens are tested under varying operating conditions to identify the most significant factors influencing wear behaviour, including reinforcement composition, applied load, and sliding speed. The optimized parameters obtained from this study are expected to enhance the wear performance of ZA-27 alloy composites and provide valuable insights for their potential applications in the aerospace, automotive, and electronics industries.
Numerical Solutions of Chen Dynamical System by Quintic B-Spline Method
We propose a novel method based on quintic B-spline functions to obtain numerical solutions of the three-dimensional Chen chaotic system. The Chen system has applications in secure communication, modeling complex natural processes, and image encryption, due to its chaotic and complex dynamics. It can also be used to design and analyze control systems, optimize performance, and study physical phenomena through its mathematical model. Due to its various applications, solutions of this dynamical system are very important. Building on cubic B-spline formulations, the method yields dynamical properties that confirm its high efficiency for such models. Chaotic systems are sensitive to initial conditions. This physical behaviour is very well captured by the proposed numerical scheme. Computed results have been presented in tabular forms and figures. All computations have been carried out in 5.11 Dev C++. For numerical simulations, Matlab software has been used
Impact of Fog Computing as an Extension of Cloud Computing
Fog Computing has emerged as a promising extension of Cloud Computing to address the inherent limitations of centralized cloud architectures, particularly in latency-sensitive and bandwidth-intensive applications. By bringing computation, storage, and networking resources closer to the data source at the network edge, fog computing significantly reduces latency, minimizes bandwidth consumption, and enhances real-time decision-making capabilities. This paradigm shift enables efficient processing of massive data generated by Internet of Things (IoT) devices, smart cities, autonomous vehicles, and industrial automation systems. The integration of fog and cloud computing creates a hierarchical architecture that leverages the strengths of both paradigms—low-latency processing at the edge and powerful computational capabilities in the cloud. This paper examines the key impacts of fog computing, including improved Quality of Service (QoS), enhanced security and privacy, energy efficiency, and scalability. It further discusses the challenges such as resource management, security concerns, and standardization issues. The study highlights how fog computing complements cloud infrastructure, paving the way for next-generation distributed computing environments in the era of 5G/6G and massive IoT deployment
Best Practices and Strategies for Implementing Hybrid Cloud Models
Hybrid cloud models provide a strategic approach that integrates the security and governance of private clouds with the scalability and cost-effectiveness of public clouds. The rapid spread of cloud computing has compelled organizations to adopt hybrid cloud strategies. This research aims to examine the most effective ways and strategies for the successful adoption of hybrid cloud architectures in modern enterprises. It examines critical variables such as task allocation, data sovereignty, interoperability, security frameworks, and governance requirements. The study highlights critical issues like integration complexity, latency hurdles, and vendor lock-in, while offering validated solutions via the use of strong cloud orchestration, automation, and standardized application programming interfaces (APIs). Emphasis is put on the utilization of multi-cloud management tools, zero-trust security frameworks, and ongoing compliance monitoring. This paper provides IT directors with pragmatic ideas for developing resilient, flexible, and future-ready hybrid cloud infrastructures. These ideas are derived from empirical case studies and industry standards. The findings suggest that a well-executed hybrid cloud strategy may significantly enhance operational flexibility, optimize expenses, and accelerate digital transformation initiatives.
Stability of a Functional Equation Involving Mean Sum of Consecutive Varibales with Quadratic Property
In, this paper we establish Ulam-Hyers stability of quadratic functional equation involving Mean sum of functions of consecutive variables of the form in Banach space via direct and fixed point method.
Governance of Data and Security in Cloud Environments
The rapid adoption of cloud computing has revolutionized data storage, processing, and accessibility, enabling organizations to achieve unprecedented scalability, flexibility, and cost efficiency. However, this shift introduces complex challenges in data governance and security, as data is often distributed across multi-cloud and hybrid environments, governed by shared responsibility models between providers and users. Effective governance ensures data quality, integrity, availability, and compliance with stringent regulations (e.g., GDPR, CCPA, HIPAA), while robust security measures protect against breaches, unauthorized access, data sovereignty issues, and emerging threats like AI-driven attacks and quantum risks. This paper examines the governance of data and security in cloud environments, highlighting key frameworks such as role-based access control (RBAC), encryption at rest and in transit, data classification, continuous monitoring via Cloud Security Posture Management (CSPM), and Data Security Posture Management (DSPM). It addresses persistent challenges, including misconfigurations (responsible for the majority of cloud security failures), compliance complexities in multi-cloud setups, privacy concerns in dynamic infrastructures, and the amplified attack surface from APIs and decentralized storage. Drawing on recent advancements, the discussion incorporates innovative approaches like blockchain for audit trails, automated policy enforcement, and AI-enhanced threat detection to foster resilient, adaptive governance. Furthermore, emerging trends such as artificial intelligence (AI), machine learning (ML), and automation in compliance management are explored, offering insights into future directions in the field. This paper serves as a valuable resource for researchers, policymakers, and practitioners interested in understanding the evolving landscape of data governance and compliance in the cloud.
Control Chart Analysis for a Bulk Arrival, Non-Markovian Queue, Random Breakdown, Delay Time, Different Vacation Policies and a Stand by Server with State Dependent Restricted Admissibility of Customers
The content of the article is a single server bulk arrival non-Markovian queue. The arrival process is a compound Poisson process with arrival size is a random variable follows a discrete distribution. The customers wait in a queue of infinite capacity if the service is not immediate. In queue the first in first out queue discipline is applied. At each service completion point the server apply two different vacation policies. Based on these two models are defined and analysed. The vacation period follows general distribution. The customers are given service singly; the service time follows general distribution. When the server is busy, the server may breakdown, the number of breakdowns follows exponential distribution. After breakdown, there is a potential delay before the repair process starts. The delay period and the repair period are generally distributed. After vacation ends, the server may continue the vacation, called extended vacation follows general distribution. The server is on vacation or repair there is a standby server, the server is service time follows exponential distribution. In addition, the arriving group will be restricted to join the system with state dependent parameter. The model is analysed by obtaining the probability generating function of the number of customers in the queue at various server’s states. Numerical illustrations are provided. Control chart analysis is carried out.
Wild Bird Species Identification Using ResNeXt50 Feature Extraction and Random Forest Classification
Accurate recognition of wild bird species from audio signals is essential for ecological monitoring and biodiversity conservation. This paper presents a hybrid ensemble-based system for bird species identification, aiming to improve recognition performance and robustness in real-world environments. The proposed approach utilizes a pre-trained ResNeXt50 deep learning model for feature extraction, followed by a Random Forest classifier for final classification. By combining deep feature learning with ensemble learning, the system achieves improved accuracy, robustness, and generalization across diverse field conditions. Furthermore, a Flask-based web interface with secure authentication is developed to facilitate system testing and performance analysis. Experimental results demonstrate that the proposed hybrid ensemble model outperforms standalone lightweight CNN models and is effective for real-time bird species recognition
Intelligent Spectrum Sensing for CR-VANETs: A Machine Learning Approach for Mobility and Security Challenges
Cognitive Radio-enabled Vehicular Ad Hoc Networks (CR-VANETs) are emerging as a vital solution to address spectrum scarcity in intelligent transportation systems. Traditional Dedicated Short-Range Communication (DSRC) suffers from fixed spectrum allocation, making it inadequate for the dynamic and high-bandwidth requirements of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communications. To overcome these challenges, this work presents an intelligent spectrum sensing framework that integrates machine learning (ML), trust management, and cooperative sensing to improve detection reliability, throughput, and security in highly mobile vehicular environments. The proposed model formulates spectrum sensing as a binary hypothesis test under Rayleigh fading and evaluates detection and false alarm probabilities using energy detection. Machine learning models including logistic regression, support vector machines, decision trees, random forests, and K-nearest neighbors are employed to optimize adaptive thresholding, sensing time, and mobility-aware performance. Gradient boosting predicts primary user (PU) activity, while Q-learning-based trust mechanisms mitigate malicious attacks such as Primary User Emulation (PUEA) and Spectrum Sensing Data Falsification (SSDF). KMeans clustering further enables localized, delay-sensitive decision-making. Simulation results demonstrate significant improvements in detection accuracy, reduced false alarms, enhanced throughput, and strong resilience against adversarial attacks, making the framework scalable and practical for next-generation vehicular networks.
Comprehensive Review of Electric Vehicle Technologies: Battery Advancements, Charging Infrastructure and Future Prospects
Electric vehicles (EVs) constitute a revolutionary change in the field of transportation technology, which is associated with important environmental and financial benefits when compared to traditional vehicles with internal combustion engines (ICE). This is due to the fact that this review focuses on a thorough analysis of the present and future of the electric vehicle technologies with specific attention given to battery technologies, charging infrastructure, and the market trends. Recent advancements since 2021 to 2026 have been synthesized in the paper to examine several architectures of EVs such as Battery Electric Vehicles ( BEVs ), Hybrid Electric Vehicles ( HEVs ), Plug-in Hybrid Electric Vehicles ( PHEVs ) and Fuel Cell Electric Vehicles ( FCEVs ). Emerging battery technologies which include solid-state batteries, sodium-ion batteries, lithium-sulfur batteries and metal-air batteries are given particular attention. The update on the charge infrastructure advancement between AC Level 1 and DC Fast charging is explained and the recent advancement on ultra-fast charging networks. The prevailing market trends indicate that the global battery technology market of EV is estimated to be 98.65 billion dollars in 2025, and will be 156.95 billion dollars in 2031[1]. In this paper, the author has named the main technological obstacles as range constraints, charge time, battery prices, and infrastructure shortages and provided the hopeful solutions as solid-state batteries designed by Toyota to achieve 750-mile range and 10-minute charge time by 2027-2028[2]. The conclusion of the analysis is that further development of battery chemistry, the extension of charging systems, and the relevant governmental policies will stimulate EV adoption and fundamentally transform the automotive industry in the world.
A Cloud-Based Hybrid Deep Learning Framework for Automated Skin Cancer Classification Using the ISIC Dataset
Early identification of malignant skin lesions is critical for reducing mortality associated with skin cancer. Manual examination through dermoscopy followed by biopsy, although clinically reliable, is time-consuming and dependent on specialist availability. This research designs a hybrid neural network architecture aimed at enabling automated large-scale detection of skin cancer through multi-class analysis of dermoscopic images collected from the ISIC archive. The proposed model integrates two complementary convolutional neural network backbones— Xception and ResNet50—initialized with ImageNet weights and configured in a parallel feature extraction architecture. Preprocessed images are resized and normalized before being passed through both networks, where high-level feature representations are extracted and concatenated into a unified descriptor. The combined feature representations are subsequently passed through a series of dense layers integrated with batch normalization, weight regularization, and dropout mechanisms to improve model stability and prevent overfitting. The final classifier assigns each input image to one of four diagnostic categories: Actinic Keratosis, Basal Cell Carcinoma, Melanoma, or Squamous Cell Carcinoma. Performance assessment conducted on 1,866 validation samples indicates stable and reliable predictive outcomes across all lesion classes. A web-based interface developed using Streamlit enables real-time inference, facilitating practical deployment as a clinical decision- support tool. The results indicate that combining heterogeneous CNN architectures improves robustness and discriminative capability in automated dermatological image analysis.
Skin Cancer Classification; Hybrid Deep Learning; Transfer Learning; Xception; ResNet50; ISIC Dataset; Dermoscopic Image Analysis; Feature Fusion; Medical Image Processing; Clinical Decision Support System
Deep Learning Based Identification and Categorization of Transmission Line Fault
Fast and accurate fault identification is instrumental in the reliable functioning of power trans mission systems. When done promptly, it can significantly limit the damages to equipment as well as power interruptions. Typically, Identification and categorization of faults Techniques are founded on mathematical models and threshold operations. However, these methods experience difficulties when dealing with varying system conditions, noise, and non-linear behavior. This study introduces a deep learning based solution to the problem of the identification and classification of transmission line faults. The approach involves gathering voltage and current signals from transmission lines under both ideal and flawed circumstances. The signals are then reprocessed and fed into They are trained using deep learning models like Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). These models are capable of automatically extracting the most relevant features from the time, domain and frequency, domain representations of the signals, thereby obviating the need for manual feature engineering. The developed network can not only locate a fault but also identify its category, For instance, single line, to, ground, double line, to, ground, and three-phase faults. Simulation studies conducted under varying fault locations, fault resistances, and noise levels demonstrate that the proposed deep learning framework achieves high accuracy, robustness, and faster response compared to conventional methods. The results confirm that deep learning techniques provide an effective and reliable solution for real-time fault identification and categorization in modern power transmission systems.
Stress and Coping Mechanism in Parents of Children with Autism Spectrum Disorder: A Comparative Systematic Review across World and Global Perspectives
Background: The aim of review is to provide a summary of stressors and coping approaches adopted by parents and of children with autism spectrum disorder. Methods: This comparative review searaches using electronic literature databases were conducted to retrieve peer-reviewed articles published from 2015 to 2025 on the topic of Autism spectrum disorder related parental stress and coping. The database searched was Scopus, ScienceDirect, Elsevier, Springer, PubMed, Google Scholar. 44 papers that met inclusion criteria (i.e., (1) study samples included parents/caregivers of children with a diagnosis of any category of ASD, and (2) studies have examined either (i) used standardized and validated tool or (ii) individual factors affecting parental coping; or(iii) psychosocial outcomes of parents in relation with coping with parental stress.3) Full text in English, examined coping mechanism and stress of ASD parents). Studies found were conducted in countries according to 6 continents (Asia=19, Europe14, North America=2, South America=5, Australia=2, Africa-2) but none from some countries which was not met inclusion criteria. Results: Across the studies some main stressors were found to be associated with parental stress: financial burden, self-criticism, intensity of symptoms of their children, child’s inappropriate behaviors, child’s future concern, concern about child’s communication and social skills, stigmatization (from society and neighbors), poor family functioning, increased workload, and social withdrawal. In this study coping strategies/mechanisms were found mostly used coping strategies/mechanisms: social support, problem focused, emotion focused, acceptance, positive reappraisal, religious belief, denial, social withdrawal, self-blame, emotion expression. Conclusion: The limitations in the current review were identified. The limited number of studies available from the search suggest a need for expand reaches on ASD and parenting stress, coping mechanism in the Australia, Africa, North America. The identified stressors may show a need when sociocultural makers for mental health professionals to support parents of children with ASD and help the parents how they changed their maladaptive coping strategies into adaptive and positive coping mechanism
Gendered Identities Across Cultures: Eunice de Souza and Dorothy Parker
This paper undertakes a comparative study of Eunice de Souza and Dorothy Parker to examine the construction of gendered identities across distinct cultural contexts—postcolonial India and early twentieth-century America. Using a gender and cultural studies framework, the paper analyses how both writers depict women negotiating patriarchal norms embedded in family structures, romantic relationships, and social expectations. De Souza’s poetry foregrounds the cultural regulation of female sexuality and respectability within Indian middle-class and religious settings, while Parker’s work explores the emotional labour and vulnerability experienced by women in modern urban America. Despite their differing historical and cultural locations, both writers represent gendered identity as shaped by social surveillance and internalised norms rather than overt coercion. The paper argues that their texts function as cultural documents that expose gender as a socially constructed and culturally mediated category. By placing De Souza and Parker in dialogue, the study highlights transnational continuities in women’s marginalisation while also attending to culturally specific forms of resistance and self-articulation.
An Ensemble Feature Optimization and Xtream Learning for High-Accuracy Software Reliability Modeling
Software reliability is the domain that delivers accurate, reliable, and high-quality software by estimating faults early in development. Many existing reliability approaches have failed to estimate faults, errors, and expectations in software development. The main reasons are advanced programming languages, high-level features, and complex connections among several software metrics, which have a significant impact on software development performance. These issues are addressed by presenting the Ensemble Feature Optimization and Xtream Learning (EFXLM) framework for high-accuracy software reliability modeling. The proposed EFXLM is an integrated model that combines the Whale Optimization Algorithm (WOA) to estimate failure rate, using optimized tuning parameters to achieve accurate defect detection. To improve WOA performance, a Deep Extreme Learning Machine (DELM) is proposed to reduce failure rates by leveraging multiple hidden layers. The experiments are conducted on two benchmark datasets from the NASA and PROMISE repositories to evaluate the algorithms' performance. Performance can be measured as the failure prediction rate in binary classification.
AI-Powered Secure Network for Intelligent Threat Detection and Automated Mitigation Using Machine Learning
The rapid escalation of cyber threats has exposed critical limitations in traditional security mechanisms, which frequently fail to address evolving and sophisticated attack vectors. This research presents an AI-powered network security framework utilizing supervised machine learning algorithms specifically Random Forest and XGBoost for real-time vulnerability detection and automated threat mitigation. The architecture consists of three modular components: (i) a real-time traffic monitoring and AI-driven classification engine, (ii) a Node.js backend for automated mitigation and secure logging, and (iii) an interactive React.js dashboard for visualization and system control. Emphasizing scalability, modularity, and secure inter-component communication, the framework enables adaptive threat response via rule-based automation. Extensive experimental evaluation on benchmark intrusion detection datasets demonstrates a detection accuracy of 94.6% and a false positive rate below 6%, with robust performance under heavy network loads. Furthermore, the system establishes a foundation for integrating deep learning based anomaly detection, block chain enabled auditability, and cloud-native deployment. The results underscore the potential of AI-driven architectures to deliver proactive, scalable, and resilient cybersecurity solutions for contemporary digital ecosystems.