Browse Items (16481 total)
Sort by:
-
Coach berth with foot assembly to climb onto the berths /
Patent Number: 337454-001, Applicant: Gyan Ganga Institute of Technology and Sciences. -
A hand glove-based system and method for allowing physically-impaired persons to communicate by identifying speech/messages /
Patent Number: 202221053593, Applicant: Hemant Verma.The present invention generally relates to a hand glove system for allowing physically impaired persons to communicate by identifying speech and messages comprises a hand glove worn on a user hand and fitted with a plurality of micro push buttons configured with alphabets and numbers marked in each of the plurality of push buttons for generating a message signal; a GPS device coupled to the hand glove for detecting real time geolocation of the user; a control unit electrically coupled to the plurality of micro push buttons for converting specified alphabet or number from the received message signal; and a communication device for transferring the converted message signal along with real time geo-location of the user to a registered person/local guardian device to ask for a help in case of an emergency. -
Machine Learning and Ensemble Models for Hazardous Asteroids Prediction
The prediction of hazardous asteroids near Earth is critical for planetary defense and avoiding any possible impacts. This study investigates the use of five ensemble models, XGBoost, Gradient Boost, CatBoost, Voting Classifier, and Random Forest, as well as four standalone machine learning models, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Decision Tree, to improve the prediction accuracy of identifying potentially hazardous asteroids. With 92% accuracy and 91% precision, Random Forest performed better than other models. It was the preferred choice for predicting hazardous asteroids because of its capacity to handle the hugedatasetwith efficiency and its ability tomanage non-linear data patterns. Additionally, XGBoost and CatBoost providedhigh accuracy at lowcomputational costs, making them suitable for real-time monitoring. KNN, on the other hand, did not perform well, and SVM's high processing time made it less useful. In particular, Random Forest ensemble modelperformed better at predicting hazardous asteroids. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms
Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Review on Image Processing-Based Building Damage Assessment Techniques
Quick damage assessment is essential for starting efficient emergency response operations following natural calamities or any other kind of disasters. After a disaster, it is crucial for rescue departments to produce judgments and distribute the resources based on a fast retrieval of precise building damage status. A ground survey is used to implement traditional building assessment, and this is labor-intensive, dangerous, and time-consuming. Studies on building damage extraction over the past few decades have generally concentrated on localizing and evaluating the destructed structures, analyzing the ratio of damaged constructions, and determining the sort of destruction each construction has sustained. Recent research trends are mainly concentrated on the utilization of data collected from multiple sensors for the damage assessments of buildings. Each stage of digital image processing can be carried out in multiple ways and several novel ideas are emerging every single day. This paper reviews the various damage assessment techniques in the different steps of digital image processing. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Advancing Building Damage Classification Accuracy through Machine Learning-Based Model Design using High Resolution Remote Sensing Images
The ability to evaluate the damage to buildings both accurately and precisely is essential for disaster recovery, planning, and rescue services. This paper proposes a new approach based on integrating machine learning algorithms in building damage classification. To achieve higher precision in classifying the level of building damage, this research proposes a new technique that employs machine learning strategies. The researchers were able to train the model to be able to differentiate the different levels of building damage and the feature extraction was performed through machine learning. The model effectively extracts and learns multiple complex signals which represent different degrees of damage from a well picked database which include several degrees of damage. In a single pass, the Siamese U-Net can perform feature extraction and similarity measurement between two different images. The efficiency and effectiveness of the Siamese U-net model can be increased by reducing inference time, thus increasing its ability to deliver faster predictions while also improving its accuracy. The suggested Enhanced U-Net (EU-Net) could greatly increase the accuracy of building-level classification. As it turned out, the results are very promising and reach beyond traditional approaches with bringing more sample opportunities of machine learning integration in the building damage assessment context. Additionally, this study believes that the accuracy of building damage classification can be further enhanced demonstrating the usefulness of machine learning in disaster management. 2025 World Scientific Publishing Company. -
Remote sensing data analyzed by machine learning to predict structural changes
Natural disasters can cause extensive structural damage, necessitating rapid and reliable post-event assessment to support emergency response and recovery planning. Although several methods exist for pixel-level damage classification using post-disaster imagery, translating these outputs into meaningful, building-wise assessments remains challenging. Building-level damage prediction provides more interpretable insights, enabling a clearer estimation of the severity of impact on individual structures and a comprehensive understanding of the overall destruction. This information is crucial for quantifying damage magnitude and prioritizing relief operations. This paper proposes Damage Estimation U-Net (DE-U-Net), a deep learning framework designed to estimate structural damage across four classes: No Damage, Minor Damage, Major Damage, and Destroyed. The model is trained on the xBD dataset to learn representative damage patterns. DE-U-Net is developed by integrating a modified Siamese U-Net with a Damage Ratio Analyzer (DRA) algorithm for building-level damage conversion. The DRA algorithm comprises three components: (1) Connected Component Analysis (CCA) to transform pixel-level predictions into building-level predictions (2) size filtering to remove noise and eliminate small artifacts, and (3) a damage estimation module to compute the number of pixels corresponding to each damage class per building. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images
We propose the development of a robust Enhanced U-Net framework for detecting building objects in images compromised by collapse. Traditional approaches often struggle to identify smaller buildings obstructed by taller structures, trees, or cloud coverage. However, recent advancements in machine learning algorithms present promising opportunities to address these challenges and improve the accuracy of building object detection and damage assessment. The proposed method employs the Siamese U-Net framework, enhanced with novel machine learning algorithms to overcome limitations in existing methodologies and increase the accuracy and reliability of damage assessment, even in complex scenarios. By using augmented satellite images during testing and lowering the building threshold value, our model can accurately predict damaged buildings and retrieve the footprints of smaller structures. The results of this research will advance image analysis techniques, especially in scenarios where collapsed structures pose significant identification and damage assessment challenges. This will be invaluable for government disaster management agencies, insurance companies, and other related organizations. 2025 World Scientific Publishing Company. -
Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes
Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn't work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array. 2023 IEEE. -
Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs
Sentiment analysis can be considered a major application of machine learning, more particularly natural language processing (NLP). As there are varieties of applications, Sentiment analysis has gained a lot of attention and is one among the fastest growing research area in computer science. It is a type of data analysis which is observed from news reports, user reviews, feedbacks, social media updates etc. Responses are collected and analyzed by researchers. All sentiments can be classified into three categories-Positive, Negative and Neutral. The paper gives a detailed study of sentiment analysis. It explains the basics of sentiment analysis, its types, and different approaches of sentiment analysis. The recent tools and APIs along with various real world applications of sentiment analysis in various areas are also described briefly. 2020 IEEE. -
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved. -
Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network
In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification. 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved. -
A Comprehensive Study on Computer-Aided Cataract Detection, Classification, and Management Using Artificial Intelligence
The day-to-day popularity of computer-aided detection is increasing medical field. Cataract is a main cause of blindness in the entire world. Compared with the other eye diseases, computer-aided development in the area of cataract is remaining underexplored. Several researches are done for automated detection of cataract. Many study groups have proposed many computer-aided systems for detecting cataract, classifying the different type, identification of stages, and calculation of lens power selection prior to cataract surgery. With the advancement in the artificial intelligence and machine learning, future cataract-related research work can undergo very useful achievements in the coming days. The paper studies various recent researches done related to cataract detection, classification, and grading using various artificial intelligence techniques. Various comparisons are done based on the methodology used, type of dataset, and the accuracy of various methodologies. Based on the comparative study, research gap is identified, and a new method is proposed which can overcome the disadvantages and gaps of the studied work. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Certificate Generation and Validation Using Blockchain
Verifying academic credentials is a standard procedure for employers when making job offers. After the interview procedure is complete, the employer takes a long time to supply the offer letter. The employer must have the certificate authenticated by the organization that issued it to confirm its originality. While confirming the authenticity of a certificate, the employer takes a long time. The selection procedure takes longer overall because of the long process involved in certificate verification. Blockchain offers a verified distributed ledger with a cryptography technique to combat academic certificate forgery to address this issue. The blockchain also offers a standard platform for document storage, access, and minimization of verification time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
-
Risk Factor Based Stage Advancement Prediction of Cataract Using Deep Learning Techniques
In modern world, Cataract is the predominant causative of blindness. Treatment and detection at the early stage can reduce the number of cataract sufferers and prevent surgery. Two types of images are generally used for cataract related studies- Retinal Images an Slit lamp Images. The quality of Retinal images is selected by utilizing the hybrid naturalness image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is and Deep newlinelearning convolutional neural network (DCNN) categorizes the images based on quality newlinescore. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid GMHE-HF) is utilized for enhanced noise filtering. The Slit lamp image quality selection is done using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) model. Further a new algorithm Normalization based Contrast limited adaptive histogram equalization (NCLAHE) is used for image enhancement. Images are pre-processed utilizing the wiener filtering (WF) with Convolutional neural network (CNN) with adaptive atom search optimization (CNN-AASO) for removing the noise. Further, the denoised image is enhanced by Gaussian mixture based contrast enhancement (GMCE) for contrast enhancement. The cataract detection and classification is performed using two phases. In phase I, the cataract is detected using Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model. Phase II uses slit lamp images and detects the type and grade of cataracts through proposed Batch Equivalence ResNet-101 (BE_ResNet101) model.This work also proposes the risk factors for cataracts and classify the cataracts risk using deep learning models. The dataset is pre-processed by missing values and the string values are converted into numeric values. -
Di-cationic ionic liquid catalyzed synthesis of 1,5-benzothiazepines
A simple and elegant method for the synthesis of 1,5-benzothiazepines has been developed using di-cationic liquid as a solvent cum catalyst by the reaction of o-aminothiophenol with a variety of chalcones under mild reaction conditions. Furthermore the reusability of the catalyst has also been studied for three cycles. All the reactions are proposed to proceed through a 1,4-conjugate Michael addition followed by a cyclo-condensation reaction. 2018, Chemical Publishing Co. All rights reserved. -
Exploration of the effects of anisotropy and rotation on RayleighBard convection of nanoliquid-saturated porous medium using general boundary conditions
This paper presents an analysis of RayleighBard convection (RBC) of a Newtonian-nanoliquid-saturated anisotropic porous medium in the presence of rotation (RayleighBardTaylor convection). The investigation is performed using non-classical boundary conditions. The effect of various parameters on the onset of convection is presented graphically. The system sees stabilisation due to an increase in the rotation rate and thermal anisotropy parameter whereas the system destabilises due to an increase in the mechanical anisotropy parameter. The results of 82 limiting cases can be extracted from the current work. The results of free-free, rigid-free and rigid-rigid isothermal/adiabatic boundaries are obtained from the present study by considering appropriate limits. The results of the limiting cases of the present study are in excellent agreement with those observed in earlier investigations. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Predicting the Stock Markets Using Neural Network with Auxiliary Input
Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.