International Journal of Engineering and Science Research
Changes in Butterfly Fauna at Different Altitudes in Central Jharkhand
This study investigates the changes in butterfly fauna across different altitudinal levels in Central Jharkhand. Butterflies, being sensitive bioindicators, provide valuable insights into the health and diversity of ecosystems. The research aimed to document the variations in species composition, abundance, and diversity of butterflies at varying altitudes, ranging from lowland forests to high-altitude regions. Field surveys were conducted over a year, covering three distinct altitudinal zones. Standardized methods, including transect walks and visual counts, were employed to record butterfly species and their abundance. The results revealed significant variations in butterfly diversity and composition across the different altitudes. Lowland areas exhibited higher species richness, with a dominance of generalist species, while mid-altitudinal regions showed a mix of both generalist and specialist species. High-altitude zones, characterized by harsher climatic conditions, had lower species diversity, with the presence of altitude-specialist species. Environmental factors such as temperature, humidity, and vegetation type were found to influence butterfly distribution and diversity. This study highlights the importance of altitudinal gradients in shaping butterfly communities and underscores the need for conservation efforts in diverse habitats.
Butterfly diversity, Altitudinal gradients, Ecosystem health, Species composition, Conservation strategies.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Impact of Content, Teachers and Academic Environment on Students’ Academic performance
The study investigates the impact of content, teachers, and the academic environment on students' academic performance, aiming to understand the multifaceted factors influencing educational outcomes. This research employs a mixed-methods approach, combining quantitative data from student performance metrics and qualitative insights from surveys and interviews with students and educators. The analysis focuses on three key components: the relevance and clarity of educational content, the effectiveness and engagement of teachers, and the overall academic environment, including facilities and support systems. Results indicate that high-quality content, delivered in an engaging and comprehensible manner, significantly enhances student performance. Furthermore, teachers' expertise, teaching methods, and their ability to create an inclusive and motivating classroom atmosphere play a crucial role in student achievement. The academic environment, characterized by adequate resources, a supportive learning culture, and positive peer interactions, also contributes substantially to academic success. The study underscores the interconnectedness of these factors, suggesting that improvements in content delivery, teacher training, and academic infrastructure are essential for optimizing student performance.
Academic Performance, Educational Content, Teacher Effectiveness, Academic Environment, Student Achievement.
Smart Human Action Monitoring Using RGB And Motion Signals
Human action recognition is a vital component of smart monitoring systems, with applications in healthcare, surveillance, and intelligent indoor environments. This project presents a smart human action monitoring system that relies exclusively on RGB images to detect and classify human activities in real time. The system processes image frames to extract essential features such as body posture, joint positions, and movement patterns. These features are then analyzed to recognize common human actions, including sitting, walking, running, drinking, and other daily activities. By leveraging advanced image processing and deep learning techniques, the system achieves high accuracy, robustness to varying lighting conditions, and efficiency suitable for real-time deployment. Experimental results demonstrate the system’s ability to monitor human activity reliably, providing a practical solution for indoor action recognition and smart environment applications.
Human Action Recognition, RGB Image Processing, Smart Monitoring System, Deep Learning, Real-Time Activity Detection, Human Activity Classification, Computer Vision, Indoor Surveillance, Image-Based Recognition, Motion Analysis, Pose Estimation, Intelligent Monitoring Systems, Activity Recognition, Artificial Intelligence.
Shahbaz Ahmed, Asim Ahmed Khan, Syed Maqdoom Masroor Abrar
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Fort2bck: Hybrid Cryptographic Validation For Robust Healthcare Data Protection
Fort2BCK is a robust security framework designed to address major vulnerabilities in healthcare blockchain systems, including data tampering, unauthorized access, and limitations in consensus protocols. The framework introduces a dual-layer verification mechanism that strengthens existing consensus algorithms using advanced cryptographic techniques such as RSA, ECDSA, and Zero-Knowledge Proofs (ZKPs). This additional security layer improves authentication, auditability, and resistance against malicious activities within system.
Healthcare Blockchain, Fort2BCK, Cryptographic Validation, RSA, ECDSA, Zero-Knowledge Proofs (ZKP), Data Security, Consensus Protocols, Blockchain Security, Healthcare Data Protection.
Enhanced YOLOv11m for Real-Time Multi-Scale Traffic Detection under Haze Conditions
In intelligent transportation systems, accurate traffic object detection plays a vital role in improving road safety, traffic monitoring, and autonomous driving applications. However, detecting vehicles, pedestrians, and other road objects under hazy weather conditions remains a challenging task due to reduced visibility, low contrast, blurred object boundaries, and background noise. Existing lightweight object detection models such as YOLOv11n provide real-time performance but often suffer from reduced detection accuracy in adverse weather environments, especially when dealing with multi-scale objects and complex traffic scenes. To address these limitations, this project proposes an enhanced object detection model named Proposed v11m, developed based on the YOLOv11n framework. The proposed system introduces several architectural improvements to enhance feature extraction and multi-scale representation while maintaining lightweight computational complexity. Firstly, an Attention-Gate Convolution (AGConv) module is integrated into the backbone network to improve contextual awareness and suppress irrelevant background information. Secondly, a Multi-Dilation Sharing Convolution (MDSC) module is incorporated to capture features at multiple receptive fields, improving sensitivity toward objects of varying scales. Additionally, a Cross-Channel Feature Fusion Module (CCFM) is designed within the neck network to adaptively recalibrate channel-wise feature importance and strengthen feature fusion. The proposed model is trained and evaluated using traffic datasets containing haze-affected road scenes. Experimental results demonstrate that Proposed v11m achieves improved detection accuracy compared to the existing YOLOv11n model, with higher mAP values while preserving real-time inference speed. The model achieves efficient performance with only 2.6 million parameters and high frame processing capability, making it suitable for deployment on resource-constrained edge devices and embedded intelligent traffic systems. Overall, the Proposed v11m model provides an effective and robust solution for real-time traffic object detection in challenging hazy environments, contributing toward safer and more reliable intelligent transportation systems.
Traffic Object Detection, YOLOv11m, Hazy Weather Detection, Intelligent Transportation Systems, Multi-Scale Feature Extraction, Attention-Gate Convolution, Cross-Channel Feature Fusion, Real-Time Object Detection.
Mohammed Ali Rizwan, Khan Javvad Ahmed, Mohammed Mustafa Uddin
Dynamic Ransomware Detection Using Time-Based Api Call Analysis
Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity-based project developed to detect ransomware attacks in real time. The project monitors API calls generated by applications and analyses their timing behavior to identify malicious activities. Machine learning techniques are used to classify whether the behaviour is normal or malicious. Dynamic Ransomware Detection Using Time-Based API Call Analysis is a cybersecurity- based project developed to detect ransomware attacks in real time. Ransomware is a type of malicious software that encrypts user files and demands payment for recovery. Traditional antivirus systems often fail to detect newly emerging ransomware variants. This project focuses on monitoring API calls generated by applications and analysing their timing behavior. By studying the sequence and time intervals of API calls, the system can identify suspicious activities related to ransomware attacks. Machine learning techniques are used to classify whether the behavior is normal or malicious. The proposed system helps in early ransomware detection before severe damage occurs, thereby improving system security and reducing data loss.
Ransomware Detection, API Call Analysis, Dynamic Malware Analysis, Cybersecurity, Machine Learning, Time-Based Analysis, Malicious Behaviour Detection, Real-Time Threat Detection, Data Security, Malware Classification, System Monitoring, Behavioral Analysis.
Mohammed Riyaz, Syed Mukeet Uddin, Syed Mohammed Ali
GPS Threat Detection Using CG-Trained ANN Framework
The widespread dependence on Global Navigation Satellite Systems (GNSS), particularly GPS, has significantly increased vulnerabilities to intentional threats such as spoofing and jamming attacks. While various machine learning techniques—including Convolutional Neural Networks (CNNs), ensemble methods, and deep learning architectures—have demonstrated promising results in threat detection, the exploration of advanced optimization algorithms for training Artificial Neural Networks (ANNs) remains limited. This paper provides a comprehensive review of GNSS security threats and existing machine learning-based detection approaches. It proposes a novel research framework that employs the Conjugate Gradient (CG) optimization algorithm for efficient ANN training. Furthermore, it details the integration of CG-trained ANN (CG-ANN) models to achieve accurate, low-latency, and real-time detection of spoofing and jamming in GPS signals, potentially enhancing GNSS resilience in critical applications.
GNSS, GPS spoofing, jamming, artificial neural networks, conjugate gradient optimization, machine learning.
Mr. MohdTekBurhanuddin, Mr. Syed Abdul Baqi,
Secure And Transparent E‑Voting System Using Blockchain, Smart Contracts, Differential Privacy, And Self‑Sovereign Identity
Electronic voting systems have become increasingly significant in modern democratic environments due to the growing demand for transparency, scalability, and digital accessibility. Traditional voting systems suffer from challenges such as centralized control, vote tampering, lack of transparency, operational delays, and high management costs. Although blockchain technology has emerged as a revolutionary solution for secure digital transactions, many existing blockchain-based voting systems still fail to achieve strong voter privacy, efficient scalability, and regulatory compliance. This paper presents BP‑Vot‑Lite, a secure and transparent blockchain-enabled electronic voting framework integrating smart contracts, differential privacy, and Self‑Sovereign Identity (SSI). The framework ensures voter anonymity, immutable vote storage, transparent result verification, and decentralized identity management. Differential privacy mechanisms are employed to prevent reconstruction attacks while maintaining statistical accuracy in election outcomes. Experimental analysis demonstrates improved latency, enhanced privacy preservation, and reliable performance in distributed blockchain environments. The proposed architecture contributes toward developing secure, transparent, scalable, and privacy-preserving digital election systems suitable for modern e-governance applications.
Blockchain, Electronic Voting, Smart Contracts, Differential Privacy, Self‑Sovereign Identity, Hyperledger Besu, Privacy Preservation, Secure Voting, Decentralized Systems.
Mohammed Museb Ahmed, Mohammed Abdullah Shareef, Md Abubakar Siddique
Under Water Net: Efficient Visual Detection of Marine Garbage for Eco Monitoring
Marine pollution poses a severe threat to the sustainability of aquatic ecosystems and the blue economy.Effective detection and classification of underwater debris are crucial for enabling timely interventions and supporting marine conservation efforts. In this project, we present an advanced underwater garbage detection system based on YOLOv10n, a cutting-edge, lightweight object detection model optimized for resource-constrained IoT and underwater robotic platforms. Building on the challenges identified in traditional detection models—such as high computational costs and deployment complexity—we replace older backbones like CSPDarknet with the more efficient YOLOv10n architecture. YOLOv10n is designed with an emphasis on speed, low parameter count, and high accuracy, making it ideal for real-time underwater applications. Our system achieves robust debris detection with high precision, while significantly reducing memory and processing requirements, thereby facilitating deployment on embedded and mobile devices. This project demonstrates the feasibility and effectiveness of using YOLOv10n for scalable and eco-friendly marine monitoring solutions, providing a practical approach to combat marine pollution through intelligent automation.
Marine Pollution, Underwater Garbage Detection, YOLOv10n, Object Detection, Deep Learning, IoT, Edge Devices, Marine Monitoring
Sadath Fatima,Syeda Iqra Kaukab, Hufaida Umama
Machine Learning Enhanced By Sentiment Analysis For Cyberbullying Detection Using Nlp And Lstm
This research presents an automated deep learning-based methodology for detecting cyberbullying in social media text, emphasizing the practical application of the LSTM architecture combined with Natural Language Processing (NLP). Safe digital communication relies heavily on the quality and efficiency of content moderation, making accurate cyberbullying detection critical for minimizing emotional harm and improving online experiences. Traditional manual moderation methods are time-consuming, inconsistent, and prone to human error, limiting scalability in large platforms. By leveraging memory cells and gating mechanisms of LSTM, the proposed system effectively extracts complex sequential features from textual data. This enables precise differentiation between offensive and non-offensive text while maintaining computational efficiency, allowing the model to function effectively even in environments with dynamic slang and linguistic nuances. A balanced and well-curated dataset of social media text was used to train and validate the LSTM-based model. The dataset includes diverse abusive types, capturing variations in harassment, insults, and threats. The model undergoes rigorous preprocessing, including tokenization, stop word removal, and lemmatization, to improve generalization and reduce overfitting. Experimental results demonstrate that the LSTM model achieves high classification accuracy while maintaining a lightweight footprint suitable for deployment in digital scenarios. Performance metrics confirm the model's reliability in identifying offensive text, highlighting its potential to enhance automated quality control in social media moderation.
Deep Learning, Cyberbullying Detection, LSTM Architecture, Text Classification, Natural Language Processing, Sentiment Analysis, Automated Moderation.
Syed Sufiyan Uddin, Syed Shahriyar Ali, Safwan Ahmed
Crack Vision: Sophisticated Concrete Crack Identification Through Transfer Learning and Deep Learning
Crack Vision is a deep learning-powered application designed to detect and classify cracks in concrete surfaces with exceptional accuracy. Unlike traditional manual inspection methods, which are time-consuming and prone to human error, Crack Vision leverages state-of-the-art convolutional neural networks and transfer learning techniques—enhanced with alternative architectures such as EfficientNetB3—to deliver reliable, real-time predictions. The system has been trained on the METU concrete crack dataset, containing balanced sets of cracked and non-cracked surface images, enabling robust binary classification. Integrated into a user-friendly Flask web interface, Crack Vision allows users to easily upload images, preview them, and instantly receive classification results along with confidence scores. Additional visualization tools, including accuracy/loss charts and confusion matrices, provide transparency into model performance. This scalable solution offers a practical tool for engineers, inspectors, and infrastructure maintenance teams, enabling faster, more consistent assessments and contributing to improved structural safety and long-term durability in civil engineering projects.
Deep learning, Concrete crack detection, Convolutional neural networks, Transfer learning, Binary classification, Flask web interface, Structural safety, Infrastructure maintenance
Mohd Maaz Taqi , Mohd Abdul Mateen Khan , Mohammed Naveed
An Empirical Investigation into Secondary Students' Attitudes Toward ICT in Education in Alipurduar, West Bengal
Information and Communication Technology (ICT) has emerged as a transformative force in contemporary education, reshaping pedagogy and learning outcomes globally. This study empirically investigates the attitudes of secondary school students toward ICT in education in Alipurduar district, West Bengal, India a geographically and socioeconomically marginalized northern border district with a predominantly tribal and tea-garden community demography. The study adopted a descriptive survey design involving 240 students (Classes IX–X) selected through stratified random sampling from six secondary schools across government and private management categories. A validated 30-item five-point Likert-type attitude scale measuring cognitive, affective, and behavioural dimensions was administered during the 2020–21 academic session. The study hypothesized that gender and school management type do not significantly influence students' ICT attitudes. Results revealed that 76.67% of respondents held a favourable attitude toward ICT. A statistically significant difference was found between government and private school students (t=3.84, p<0.05), while gender differences were non-significant (t=1.42, p>0.05). Barriers including infrastructural deficiency and limited internet access were prominently self-reported. The study concludes that equitable ICT policy and targeted investment are critical for educationally underserved districts.
ICT attitudes, secondary students, digital divide, West Bengal, educational technology
Copyright (c) 2021 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Assessment of Alternative Fuel Performance: Emission and Efficiency Analysis of Diesel-Tyre Pyrolysis Oil Blends
As we all know the due to the scarcity of conventional fuels and the Crude oil, the price of the conventional fuel is going up day by day and there Will no more conventional fuel in future and it is also Increasing the environmental pollution by the usage Of crude oil .And by using biofuels also we are consuming the natural products Therefore in the investigation the oil taken will pyrolysis oil which will be obtained by the Pyrolysis of the waste of automobile tire , and by this the disposal of waste rubber are Becoming the threat to the environment will also be Solved. By this investigation we can not only solve the shortage of crude oil but also can reuse the waste automobile tires, in the initial stage the test will be conducted on four Strokes single cylinder diesel engine by using diesel. Experimental investigation will be carried out on the same engine by changing the crank angle and by using the tire pyrolysis oil blended with Diesel in Different proportions such as T10, T20 and T30, to Find out the performance parameters, by changing the inlet pressures as 200 bar, 220 bar and 240 at different load condition for each inlet pressure 6, 9 & 12, with crank angle 23deg standard. After performing the experiment the performance characteristics would be determined.
Tire Pyrolysis Oil, Diesel Engine, Alternative Fuel, Waste Tire Recycling, Biofuel Blends, Compression Ignition Engine, Fuel Injection Pressure, Crank Angle, Engine Performance, Emission Characteristics, Waste Rubber Management, Pyrolysis Process, Sustainable Energy, CI Engine, Fuel Blending, Renewable Energy, Thermal Efficiency, Brake Specific Fuel Consumption, Environmental Pollution Reduction, Waste-to-Energy Technology
Md Amer Ullah Baig, Syed Zubair, Mohammed Khaja Asad Uddin, Mohammed Ameerouddin
Edge-Ready Road Damage Detection Using an Enhanced YOLO with Hyperparameter Tuning
Maintaining road quality is a crucial aspect of smart city development and public safety, as damaged roads can lead to traffic congestion, accidents, and increased vehicle maintenance costs. Traditional road inspection methods rely heavily on manual surveys, which are not only time-consuming but also costly and prone to human error. With the rapid growth of urban infrastructure, the need for automated, accurate, and scalable road damage detection systems has become more urgent. Recent advances in deep learning and computer vision provide an opportunity to address these challenges by enabling real-time damage detection using high-performance object detection models. In this study, we present a road damage detection framework built on the YOLOv10n model, optimized specifically for real-time deployment on edge devices. The proposed system incorporates advanced hyperparameter tuning to achieve a balance between accuracy and computational efficiency. Unlike heavier detection models, YOLOv10n is lightweight yet powerful, making it suitable for resource-constrained environments. The framework demonstrates robust detection capabilities with a precision of 0.986, recall of 0.973, mean average precision (mAP@0.5) of 0.988, and an F1-score of 0.978, confirming its reliability in identifying different types of road damages such as cracks, potholes, and surface wear.
smart city, road quality, automated inspection, deep learning, computer vision, YOLOv10n, edge deployment, hyperparameter tuning, real-time detection, traffic safety.
Md Uzaif Noman, Syed Yaseenullah Hussaini, Mohammad Uzair
Efficient Blockchain Mechanisms for Ensuring Data Integrity in IoT Systems
In the evolving landscape of the Internet of Things (IoT), ensuring data integrity and traceability remains a critical challenge due to the vulnerability of low-power, resource-constrained devices and the use of wireless communication technologies like LoRaWAN. Traditional blockchain solutions, while promising for enhancing security, are typically unsuitable for IoT environments because of their high computational and communication requirements. To address this limitation, we propose a lightweight blockchain-based framework specifically tailored for IoT networks using the LoRaWAN protocol. Our system introduces a modified distributed ledger approach that eliminates the need for complex consensus mechanisms and resource-intensive cryptographic operations, enabling practical deployment on constrained devices. Despite LoRaWAN’s inherent star topology, our design simulates a logical peer-to-peer (P2P) communication model by linking IoT nodes through local blockchain chains. Each device maintains its own local chain of data uploads, allowing for decentralized validation and improved data traceability. Experimental evaluation demonstrates the feasibility and performance of our system in maintaining data integrity and traceability with minimal network overhead, offering an effective and scalable security solution for next-generation IoT networks.
Internet of Things (IoT), Lightweight Blockchain, Data Integrity, LoRaWAN Protocol, Local Chain Validation, Simulated P2P Consensus, Data Traceability, and SHA-256 Hashin
Mohammed Shahbaz Ul Hasan, Md Muqeeth, AyanUllah Khan
Design and Optimization of an Automated PVC Pipe Cutting System for Industrial Applications
Industrial automation has become increasingly important in modern manufacturing industries due to the growing demand for high production rates, improved quality, and reduced operational costs. Pipe cutting is one of the major operations performed in construction, plumbing, irrigation, and industrial manufacturing sectors. Conventional manual cutting methods are time-consuming and often result in inaccurate dimensions, poor surface finish, material wastage, and increased labor requirements. This paper presents the design and optimization of an automated PVC pipe cutting machine intended for industrial applications. The developed system utilizes a motor-driven cutter assembly integrated with gears, shafts, bearings, rollers, and a structural support frame. The machine was designed using CATIA V5 software and analyzed using ANSYS finite element analysis tools. Structural analysis including stress, strain, and total deformation was carried out to evaluate the reliability and safety of the machine under operating conditions. The results obtained from ANSYS confirmed that the machine operates within permissible stress limits and provides stable cutting performance with minimal vibration and deformation. The developed machine improves cutting accuracy, minimizes manual effort, reduces production time, and enhances industrial productivity. The proposed system is suitable for industrial manufacturing applications where efficient and accurate pipe cutting operations are required.
PVC Pipe Cutting Machine, CATIA V5, ANSYS, Structural Analysis, Finite Element Analysis, Automation, Industrial Manufacturing.
Mohammed Abdul Aleem Siddiqui, Mohd Mudassir Ali, Mohd Mudassir, Mohammed Omer Ali
Meta-Analysis Of Azo-Functionalized Calixarenes: Synthesis, Properties, And Antibacterial Activity
Azo-functionalized calixarenes are an increasingly attractive and structurally unique member of the supramolecular family whose functionality provides a large degree of versatility as explored in synthetic chemistry, materials science, and biomedical applications. This review is a meta-analysis of all published research work related to the synthesis, physicochemical properties and antibacterial activity of azo-substituted calixarene derivatives in three decades. Calixarenes are macrocyclic oligomers formed mainly by the condensation reaction of phenol and formaldehyde, which provide an outstanding three-dimensional hosting molecule type for azo (-N=N-) chromophoric groups forming hybrid molecules with better optic, electronic and biological properties. This meta-analysis reviews a range of peer-reviewed studies that have been systematically searched to summarize the findings related to synthetic methods, characterisation techniques including spectra analysis, host-guest interactions, photo switchable behaviour and in vitro antimicrobial activity. Above all, data compiled from more than thirty key studies demonstrate similar trends in modulatory activity as a result of increasing substitution on the azo group and/or cavity expansion of calixarenes leading to bactericidal augmentation efficacy. The review also highlights several notable gaps in available literature, especially with respect to the in vivo antimicrobial evaluation of novel compounds, detailed structure-activity relationships at the molecular level and their potential for smooth clinical translation. Together, the evidence strongly supports the conclusion that azo-functionalized calixarenes represent important new candidates for next-generation antibacterial agents and functional supramolecular materials. Finally, future perspectives are highlighted by areas of opportunities as green synthesis, drug delivery platforms and multi-drug-resistant organism targeting can be prompted in this relevant field of supramolecular chemistry.
calixarenes; azo compounds; antibacterial activity; supramolecular chemistry; host-guest interactions; macrocyclic synthesis; photo switchable materials
Synthesis And Study Of The Physicochemical And Antimicrobial Properties Of New Azo-Calix [6] Arene Derivatives
The current study describes the synthesis, structural characterisation and physicochemical and antimicrobial properties of five new azo-calix[6]arene analogues (AC6-1 to AC6-5). Compounds were synthesized through diazotization-coupling reactions on an electron-withdrawing and/or -donating substituted calix[6]arene scaffold decorated with azo aryl groups. FT-IR, ¹H NMR (including heteronuclear single quantum coherence (HSQC) and double-quantum correlation spectroscopy (DQF-COSY)), ¹³C NMR, mass spectrometry, and elemental analysis were used to confirm the structure. The physicochemical parameters were systematically determined, including the melting points (218–248°C), maximum absorbance in UV-Vis λmax (412–448 nm), log P (3.81–4.68) and solubility profiles. Thermogravimetric analysis showed decomposition onset temperatures were in the range of 248°C–279°C which indicated satisfactory stability was achieved across the series. Broth microdilution and agar diffusion methods for the antimicrobial evaluation against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Candida albicans and Aspergillus niger resulted in minimal inhibitory concentrations (MIC) as low as 2 μg/mL (AC6-5 against S. aureus) and inhibition zones with a length of up to 22.7±0.6 mm. Compound-dependent and organism-dependent variability was confirmed by statistical analysis (two-way ANOVA + Pearson correlation p < 0.001). Compound AC6-5, with its nitro-substituted azo group, showed the strongest biological activity because of an improved electron deficiency and hydrophobicity. These results position azo-functionalized calix[6]arenes as a new class of compounds with tremendous scope for antimicrobial drug discovery in a structural tuneable family.
Calix[6]arene; Azo compounds; Antimicrobial activity; Minimum inhibitory concentration; Physicochemical properties; Diazotization-coupling; Structure–activity relationship
Green Synthesis Of Bio-Active Oxygen-Bridged Complexes: A Comparative Study Of Their Antioxidant And Antibacterial Potency
Oxygen-bridged metal complexes show unique electronic properties, structural versatility, and bioactivity and are increasingly being studied in bioinorganic chemistry. Conventional preparative methods usually involve the use of toxic solvents and chemical reductants, which often raise sustainability issues. Herein, we present the green synthesis approach of three μ-oxo bridged binuclear complexes of iron (III), copper (II), and zinc (II) / using aqueous extract of Azadirachta indica and Ocimum sanctum as biological-reducing and capping agents. The synthesized complexes were characterized by UV-Vis and FTIR spectroscopy and were screened for antioxidant activity using DPPH and ABTS radical scavenging assays and antibacterial activity against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa via the agar well diffusion method. The copper-based [Cu–O–Cu] complex showed the highest antioxidant activity (IC50 36.2 μg/mL by DPPH; 31.8 μg/mL by ABTS), and the largest inhibition zones among all tested bacterial strains, with the highest activity against S. aureus (21.7 mm). The iron-containing [Fe–O–Fe] complex showed an intermediate yet stable antioxidant and antibacterial performance; while [Zn–O–Zn], had the poorest activity profile. These results not only establish a distinct order of bioactivity (Cu > Fe > Zn) but also indicate that the phytochemical-mediated synthesis produces oxygen-bridged complexes, which possess recognized therapeutic activity as well as a significantly lower environmental impact.
Green synthesis, μ-oxo bridged complexes, Antioxidant activity, DPPH, Antibacterial activity, Bioinorganic chemistry.
Deepak Kumar*, Mamta Gaur (Patel), And Om Prakash Gupta*
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
An Enhanced RNN-LSTM Model For Accurate and Real-Time Click Fraud Detection in Online
Click fraud continues to pose a significant threat to online advertising by inflating costs and diverting budgets toward illegitimate activities, necessitating more sophisticated detection methods that go beyond traditional machine learning capabilities. In this study, we propose a robust LSTM-based Recurrent Neural Network (RNN) framework specifically designed to capture subtle behavioral patterns and time-dependent features inherent in user interaction data. By implementing a comprehensive preprocessing pipeline—comprising timestamp decomposition, feature scaling, and label encoding—we ensured high-quality input representation for our model. When benchmarked against Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), our RNN-LSTM architecture demonstrated superior performance, achieving 99% accuracy alongside exceptional precision and recall scores. These results confirm that temporal modeling is highly effective for identifying fraudulent click sequences, positioning the LSTM model as a powerful solution for real-time ad verification and establishing a new benchmark for future advancements in intelligent fraud prevention.
Click Fraud Detection, LSTM (Long-Short Term Memory), RNN-Recurrent Neural Network, Sequential patterns, Temporal Modelling, Deep Learning, Real-Time Detection.
Syed Abdul Rahman, Mohammed Umair, Syed Muzammil Quadri
Deep NLP Techniques For Tweet Similarity In Fake News Detection Systems
Addressing the intricate challenge of fake news detection, traditionally reliant on the expertise of professional fact-checkers due to the inherent uncertainty in fact-checking processes, this research leverages advancements in language models to propose a novel Long Short-Term Memory (LSTM)-based network. The proposed model is specifically tailored to navigate the uncertainty inherent in the fake news detection task, utilizing LSTM's capability to capture long-range dependencies in textual data. The evaluation is conducted on the well-established LIAR dataset, a prominent benchmark for fake news detection research, yielding an impressive accuracy of 99%. Moreover, recognizing the limitations of the LIAR dataset, we introduce LIAR2 as a new benchmark, incorporating valuable insights from the academic community. Our study presents detailed comparisons and ablation experiments on both LIAR and LIAR2 datasets, establishing our results as the baseline for LIAR2. The proposed approach aims to enhance our understanding of dataset characteristics, contributing to refining and improving fake news detection methodologies by effectively leveraging the strengths of LSTM architecture.
Fake News Detection, Long Short-Term Memory (LSTM), Deep Learning, Natural Language Processing (NLP), LIAR Dataset, LIAR2 Dataset, Machine Learning, Text Classification, Fact-Checking, Misinformation Detection, Neural Networks, Social Media Analytics, Dataset Benchmarking, Artificial Intelligence, Sequence Modeling.
Ozair Mohammed Haneef, Mohammed Akif, Sayeed Ateeb Ul Hasan
Machine Learning-Driven Real-Time Battery Health Estimation for EV Battery Swapping
Electric Vehicles (EVs) are rapidly transforming the global transportation ecosystem due to their environmental advantages, reduced greenhouse gas emissions, and decreased dependence on fossil fuels. As EV adoption increases, efficient battery management has become a critical challenge, particularly in battery swapping systems where batteries are frequently exchanged and subjected to varying operational conditions. In such systems, accurate estimation of battery health is essential to ensure operational safety, reliability, and cost-effectiveness. This paper presents a machine learning-driven framework for real-time estimation of key battery health parameters, including State of Health (SoH) and Remaining Useful Life (RUL). The proposed system leverages ensemble learning techniques, specifically Random Forest Regression and XGBoost, to analyze battery operational data such as voltage, current, temperature, and cycle count. These models are capable of capturing complex nonlinear relationships among battery parameters and provide accurate predictive insights. Furthermore, the trained models are integrated into a Flask-based web application, enabling real-time predictions and user-friendly interaction. Experimental results demonstrate that the proposed system outperforms traditional rule-based and threshold-based approaches in terms of accuracy, robustness, and computational efficiency. The system achieves prediction accuracy exceeding 95% while maintaining low error rates and fast response times. The proposed solution facilitates predictive maintenance, reduces the risk of battery degradation, enhances safety, and improves the overall efficiency of EV battery swapping stations.
Electric Vehicles, Battery Health Estimation, Machine Learning, Random Forest, XGBoost, State of Health (SoH), Remaining Useful Life (RUL), Battery Swapping.
Mohd Isam Uddin, Shaik Saleem Pasha, Shaik Ibrahim
Deep Learning-Based Automated Defect Detection in Solar Cell Images
This research presents an automated deep learning-based methodology for detecting defects in solar cell images, emphasizing the practical application of the Xception architecture. Solar energy production relies heavily on the quality and efficiency of solar cells, making accurate defect detection critical for minimizing energy losses and production costs. Traditional manual inspection methods are time-consuming, inconsistent, and prone to human error, limiting scalability in industrial settings. By leveraging the depthwise separable convolutions and efficient architecture of Xception, the proposed system effectively extracts complex features from high-resolution solar cell images. This enables precise differentiation between defective and non-defective cells while maintaining computational efficiency, allowing the model to function effectively even in environments with limited hardware resources. A balanced and well-curated dataset of solar cell images was used to train and validate the Xception-based model. The dataset includes diverse defect types, capturing variations in cell surface anomalies, scratches, cracks, and other imperfections that impact performance. The model undergoes rigorous preprocessing, including image normalization, resizing, and data augmentation techniques, to improve generalization and reduce overfitting. Experimental results demonstrate that the Xception model achieves high classification accuracy while maintaining a lightweight footprint suitable for deployment in industrial scenarios. Performance metrics, such as precision, recall, and F1-score, confirm the model’s reliability in identifying defective cells, highlighting its potential to enhance automated quality control in solar panel manufacturing.
Deep Learning, Solar Cell Defect Detection, Xception Architecture, Image Classification, Computer Vision, Renewable Energy, Feature Extraction, Automated Inspection.
Mohd Zaki, Shaik Faisal, Zaafir Aziz Khan
Enhancing Precision Agriculture Pest Control: A Yolov10-Based Deep Learning Approach For Insect Detection
Precision Agriculture (PA) leverages advanced technologies to optimize resource use while preserving crop quality and yield. However, pest infestations remain a critical challenge that can undermine these benefits. Recent deep learning frameworks like YOLOv8 have shown promise in real-time insect detection, yet often remain limited to specific insect types or crops. To address this limitation and improve detection accuracy, this work explores an enhanced, generalized approach using the latest YOLOv10 object detection model. We develop and test a YOLOv10-based tool designed to detect any insect category across diverse crops, enabling broader and faster pest monitoring in the field. A comprehensive performance evaluation was conducted on a benchmark insect dataset, demonstrating notable improvements over YOLOv8, including higher mean Average Precision (mAP) scores and faster inference speeds. The findings suggest that YOLOv10's architectural advancements contribute to more robust, scalable, and real-time pest detection, offering significant potential to strengthen pest management strategies within precision agriculture.
Precision Agriculture, Pest Detection, YOLOv10, Object Detection, Insect Monitoring, Deep Learning, Real-time Detection, Crop Protection, mAP Improvement, Scalable Pest Management.
Abdul Bari, Md Sadiq Bilal, Mohammed Aijaz
IOT-Based Smart Parking System With Real-Time Slot Monitoring
Smart Parking Systems are an innovative solution designed to tackle the challenges of urban Traffic congestion and limited parking spaces. By using advanced sensors, Internet of Things (loT) technology, and data analytics, these systems help drivers quickly locate available Parking spots, reducing time, fuel consumption, and environmental pollution. The system works by using sensors installed in each parking space to detect the presence or Absence of a vehicle. The collected data is then transmitted to a central platform, which displays Real-time parking availability through a mobile or web-based application. This not only makes Parking more convenient for drivers but also improves space utilization and traffic flow in Cities. Smart Parking solutions also benefit municipalities and parking operators by providing valuable analytics for pricing, policy planning, and efficient management of parking areas. The Platform's dashboard enables monitoring, data analysis, and trend prediction, which support Better decision-making. Overall, the Smart Parking System transforms traditional parking into an intelligent, Automated, and user-friendly experience. By combining loT, automation, and smart data Management, it aims to create a cleaner, more efficient, and sustainable urban environment. Technology used: The project uses IoT, embedded systems, sensors, wireless communication, Cloud computing, data analytics, and a mobile/web application to create a real-time smart Parking system.
IoT Based Smart Parking System, Real Time Slot Monitoring, Smart Parking, Internet of Things (IoT), Parking System, System Architecture, System Design, Data Flow Diagrams, Implementation, Automation.
Syed Saad Abdul Majeed, Syed Mizbah Ur Rahman, Mohammed Faizan
Implementing Big Data Analytics in E-Commerce: Vendor and Customer View
In today’s digital era, e-commerce platforms generate massive volumes of data every second from customer interactions, product searches, transactions, reviews, and browsing histories. Managing and analyzing this vast amount of data is crucial for gaining business insights and improving user experience. This project, “Implementing Big Data Analytics in E-Commerce: Vendor and Customer View,” focuses on utilizing Big Data technologies to analyze and interpret large datasets to benefit both vendors and customers. The primary objective of this project is to design and develop a system that collects, processes, and analyzes e-commerce data using Big Data analytics tools such as Hadoop, Spark, and MongoDB. From the vendor’s perspective, the system helps in understanding sales trends, identifying high-demand products, optimizing inventory, forecasting customer needs, and planning marketing strategies. From the customer’s perspective, Big Data analytics enables personalized product recommendations, dynamic pricing, faster search results, and an overall enhanced shopping experience. The project involves four main stages — data collection, data storage, data processing, and data visualization. Data is gathered from multiple e-commerce sources such as product catalogs, user interactions, and transaction logs. The data is then stored in a distributed environment using Hadoop’s HDFS and processed through Apache Spark to extract meaningful patterns. Finally, data visualization tools like Tableau or Power BI are used to present insights in an understandable format for both vendors and administrators. In addition to analytical benefits, this system also focuses on real-time decision-making. By integrating streaming data analysis, vendors can immediately detect trends such as sudden changes in customer preferences or product popularity. This allows businesses to react quickly—adjusting pricing, restocking popular items, or offering targeted discounts. The inclusion of predictive modeling further enhances the system’s capability to forecast future sales and customer demand based on historical patterns, ensuring that vendors can plan ahead effectively.
Big Data Analytics, E-Commerce, Apache Hadoop, Apache Spark, MongoDB, HDFS, Data Collection, Data Storage, Data Processing, Data Visualization, Customer Behavior Analysis, Vendor Analysis, Personalized Recommendations, Real-Time Analytics, Predictive Modeling, Sales Trend Analysis, Demand Forecasting, Inventory Optimization, Business Insights.
Mohd Omer, Mirza Abdul Rahman baig, Mohammed Saif Uddin
AI-Driven Fake News Detection Using NLP and Machine Learning
The rapid growth of online news platforms and social media has increased the spread of fake news, leading to misinformation and public confusion. Detecting fake news manually is time-consuming and inefficient due to the large volume of digital content. This project proposes an AI-Driven Fake News Detection System using Natural Language Processing (NLP) and Machine Learning to automatically identify and classify news as real or fake. The proposed system analyzes textual news content by applying NLP techniques such as tokenization, stopword removal, and feature extraction using TF-IDF. Machine learning algorithms are trained on labeled datasets to learn patterns associated with fake and real news. The system provides accurate and fast classification, reducing human effort and improving reliability. The experimental results demonstrate that the proposed system effectively detects fake news with satisfactory accuracy. The system is scalable, cost-effective, and suitable for real-world applications such as online news platforms and social media monitoring. Index Terms— Fake News Detection, Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Text Classification, TF-IDF, Naive Bayes, Logistic Regression, Text Preprocessing, Feature Extraction, Tokenization, Stopword Removal, Misinformation Detection, Data Analysis, News Classification.
Advanced Electronic Voting Machine Using Fingerprint Sensor And Arduino
The rapid growth of electronic system has increased the need for secure, transparent, and reliable voting systems. This paper presents the design and implementation of an advanced Electronic Voting Machine (EVM) integrated with a fingerprint sensor and Arduino microcontroller to enhance and improve election security and eliminate wrong voting practices. The proposed (suggested) system uses biometric authentication to uniquely identify voters, assuring that only authorized individuals are allowed to cast a vote. The fingerprint sensor captures and verifies voter fingerprints against a pre-stored database, preventing false representation and multiple voting procedures. The Arduino platform acts as the central control unit, managing voter verification, vote recording, and system operations efficiently with minimal hardware complexity and low power consuming. Also, making the system suitable for large-scale deployment in both urban and rural areas. The projected EVM decreases human interference, and minimizes operational faults, also improves voter sureness in the election procedure. The results prove that the fingerprint-based voting system offers the high reliability, fast verification, and strong safety, and making it up-to-date self-governing elections. This project plays a vibrant role in refining the election process by minimizing human mistakes, and giving quick and precise vote counts. The system certifies that each voter can give or cast only one vote and all that data is safely recorded or stored for final process or we can say results process. Identity verification is secure and distinctive thus which may implement in mechanical device to realize high secure election. The Electronic Voting Machine using Arduino Uno demonstrates how embedded systems can contribute efficiency and transparent elections in both small-scale and large-scale applications and develop secure voting system.
Zainab Ria Rasheed, Fariha Fatima, Syeda Haleema Sadia
Intelligent Farmer Assistant And Crop Lifecycle Management Platform With Multilingual Support
Agriculture supports the livelihoods of more than 140 million households in India, yet a significant disconnect persists between the information farmers require and what is accessible to them. Existing digital advisory tools are often fragmented, linguistically restrictive, and dependent on high levels of technical proficiency, limiting their usability among smallholder and marginal farmers. These barriers are particularly evident in rural regions, where language diversity, inconsistent internet connectivity, and limited digital literacy constrain adoption.This study introduces the Intelligent Farmer Assistant and Crop Lifecycle Management Platform, a comprehensive mobile-first solution designed to provide end-to-end agricultural support. The system integrates artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to assist farmers throughout the crop lifecycle. The platform is implemented using Flutter for cross-platform mobile development, Spring Boot for backend services, and Firebase for real-time data synchronization.The system comprises six key components: (1) a convolutional neural network (CNN)-based crop disease detection module, (2) a multilingual conversational assistant, (3) a long short-term memory (LSTM)-based market price prediction model, (4) a crop lifecycle management system, (5) a location-aware weather advisory module, and (6) a carbon footprint estimation tool. The disease detection model leverages MobileNetV2 with transfer learning, trained on the PlantVillage dataset containing over 87,000 labeled images, achieving 96.5% classification accuracy. The chatbot supports Telugu, Hindi, and English through both text and voice interfaces, with an intent recognition accuracy of 93.7%. The market prediction module utilizes a stacked LSTM architecture trained on AgMarkNet data, achieving strong predictive performance (R² = 0.91, MAPE = 4.2%).
Intelligent Agriculture, Crop Disease Detection, Convolutional Neural Networks, MobileNetV2, LSTM, Market Forecasting, Multilingual NLP, Precision Farming, Digital Agriculture, Carbon Footprint, AgriTech, Smallholder Farmers
B. Lokesh, M. Rishitha, R. Harsha Vardhan, R. Mamatha
Convolutional Neural Network for Binary Brain Tumor Classification Using MRI Scans
Brain tumor detection and classification play a critical role in early diagnosis and treatment planning. In this project, we implemented a deep learning-based Convolutional Neural Network (CNN) model to classify MRI brain images into tumor and non-tumor categories. The model was trained on a pre-processed MRI dataset using image normalization and augmentation techniques to improve generalization. Our CNN architecture, consisting of convolutional, pooling, and fully connected layers, achieved a training accuracy of approximately 97–98% and a test accuracy of 92–94%.This work was inspired by the base paper “Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification”, which employed an advanced ensemble fusion approach combining CNNs, Transformers, and clinical data to achieve a classification accuracy of 93.6% with an AUC of 0.967. While our implementation focuses solely on MRI image classification with a single CNN model, the results demonstrate comparable accuracy levels. Unlike the base paper, our model does not incorporate multimodal data fusion or ensemble strategies, making it computationally simpler and more lightweight, while still achieving reliable performance for binary brain tumor detection. The findings indicate that deep learning models can effectively support medical imaging tasks, with scope for future enhancement through multimodal integration, ensemble learning, and advanced evaluation metrics such as precision, recall, and F1-score.
Brain tumour detection, Convolutional Neural Network (CNN), Deep learning, Binary classification Image preprocessing, Clinical data integration, Precision, recall, F1-score.
Mohammed Ahmeduddin Taha, Mohammed Zaid, Ibrahim Khan