Processing Algorithms Research Articles

Real-Time View Assistance for the Blind using Image Processing

Visually impaired people usually have difficulty in doing daily activities. Imagine a future where visually impaired people can seamlessly and independently identify objects and people in their environment. The aim of this research is to increase the independence and mobility of visually impaired people by developing a real-time object and person recognition system. This system uses the power of machine learning and uses computer vision techniques to accurately identify and classify objects and people in the user's environment. Through the integration of speakers or headphones, the system provides auditory feedback to the user and conveys important information about the detected object or person. By combining advanced image processing algorithms with audio output, this solution serves as a valuable tool for visually impaired people, allowing them to effectively perceive and understand their surroundings. This innovative approach demonstrates the potential of technology to bridge access gaps and empower people with visual impairments in their daily lives.

Alzhimer Detection Using CNN

Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disease. It is most popular Alzheimer disease symptoms include trouble thinking, making judgment and decision, not able to do familiar task it’s going to also cause alteration in personality and behavior .The factor of AD development is poorly known. As the sickness advances, an individual with Alzheimer disease will develop severe amnesia and lose the capacity to perform everyday activation. This incurable disease is mainly found within elderly people. Neuroimaging technique like MRI and PET scan are used for AD detection. For better result multi model neuroimaging technique are used with DL algorithm for Alzheimer classification process. In this project we are going to use MRI along with PET scan and CNN (convolution neural network) for image classification into normal cognitive (NC).

Multimodal Content Analysis Using Deep Learning

Abstract: The multimodal content analysis platform combines sentiment analysis and neural style transfertechniques to process and improve various types of digital content. The sentiment analysis module utilizes natural language processing (NLP) algorithms, such as recurrent neural networks (RNNs) or transformer models like BERT, to extract emotional signals from textual, visual, and auditory inputs. Signals are classified into predefined sentiment categories, providing granular insights into the emotional context of the content. The platform employs neural style transfer algorithms, such as style transfer networks (NSTNs) or generative adversarial networks (GANs), to transfer stylistic attributes between texts. By training on a diverse range of artistic styles, the system learns to apply these styles to input text while preserving semantic meaning. This process enhances the visual representation of textual content, making it more appealing and engaging to users.

Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions

Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

Enhancing Agricultural Sustainability: Automated Crop Disease Detection through Image Processing Techniques

Abstract: The increasing demand for sustainable and high- yielding agricultural practices has driven the need for advanced technologies in crop disease detection. This paper presents a novel approach to automated crop disease detection utilizing stateof-the-art image processing techniques. In the image acquisition phase, high-resolution images of crops are captured using modern imaging devices. These images are then subjected to preprocessing techniques, including resizing, normalization, and filtering, to enhance their quality. Feature extraction follows, where relevant information is derived from the preprocessed images using advanced image processing algorithms, such as texture analysis and color-based feature extraction. The heart of the proposed system lies in the classification phase, where a machine learning or deep learning model is trained on a curated dataset comprising labeled examples of healthy and diseased crops. The integration of this automated crop disease detection system with existing agricultural frameworks provides real-time or periodic monitoring capabilities. This integration empowers farmers with timely and precise information, enabling early intervention and targeted treatment strategies to minimize crop losses. This research contributes to the ongoing efforts in sustainable agriculture by providing a reliable and efficient solution for early crop disease detection.

Lung Cancer Detection

Abstract: This paper presents a comprehensive approach to lung cancer detection utilizing state-of-the-art machine learning techniques, specifically Convolutional Neural Networks (CNNs). Using CNN[1] the model is trained and it can detect whether the given lung cancer cell image contains cancerous cells or not. The first component of the proposed approach involves high-resolution medical imaging, such as computed tomography (CT) scans, to capture detailed anatomical information about the lungs. Image processing algorithms are applied to enhance the quality of the images and extract relevant features. Additionally, innovative three-dimensional reconstruction techniques are employed to visualize the lung tissue at a microscopic level, facilitating the identification of subtle abnormalities.

Plant Disease Detection

Abstract: This project addresses the critical challenge of plant disease detection in India, a nation where agriculture supports nearly 70% of the population. Traditional procedures for identifying plant diseases are both labor-intensive and time-consuming. To enhance efficiency and accuracy, this project introduces an innovative approach that integrates machine learning and image processing techniques. By analyzing images of leaves, the system identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters. Unlike conventional methods that depend on visual inspections or chemical processes, this automated system provides a cost-effective and efficient solution for monitoring extensive crop fields. By leveraging statistical machine learning and advanced image processing algorithms, the proposed solution ensures precise disease detection with minimized computational complexity and reduced prediction time

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Crack Detection of Structures using Artificial Intelligence System

Abstract: Structural Health Monitoring (SHM) having wider scope of operations such as structural audit, rehabilitation of structures and along with it a major scope of crack detection is also included. Effective crack detection is vital for ensuring the safety and longevity of civil engineering structures. Crack Detection in existing methods is tedious process, it requires costly equipment’s as well as time consuming, and inaccuracy in mechanical equipment’s. The technological advancements especially in the field of Artificial Intelligence (AI) maybe an acceptable solution for crack identification through mobile app using Image processing techniques. The mobile app can be used to accurately identify crack pattern and measure the size of cracks in structures (concrete and steel). The app needed to capture and process data from diverse sources, such as images, in real time. It allows for real-time monitoring of structures and ensures safety and integrity of it. The image processing algorithm is used for to accurately identifying various types of cracks in different lighting, environmental conditions, effective and optimized solution. This project explained about real-time data processing for crack detection and its challenges. This demanded robust backend infrastructure and efficient data processing techniques to deliver prompt and accurate results. User Interface and Experience developing an intuitive and user-friendly interface for the app while maintaining high performance.

Advancements in Visual Prosthetics: The Visionary Bionic Model

Abstract: The Visionary Bionic Model represents a paradigm shift in the field of visual prosthetics, offering new hope to individuals afflicted by retinal degenerative diseases. This journal paper provides a comprehensive overview of the foundational framework, operational mechanics, and advancements associated with the Visionary Bionic Model. Beginning with a historical context, the paper traces the evolution of bionic eyes from early conceptualizations to contemporary innovations. It explores the technological underpinnings of the Visionary Bionic Model, including microelectrode arrays, image acquisition systems, and signal processing algorithms. Clinical trials and case studies are examined to evaluate the safety and efficacy of the Visionary Bionic Model in restoring sight to individuals with visual impairments. The paper also discusses challenges and limitations inherent in current bionic vision systems, such ` resolution and compatibility issues, and proposes avenues for future research and development. Ethical considerations surrounding accessibility, affordability, and informed consent are addressed, emphasizing the importance of responsible innovation. Ultimately, the Visionary Bionic Model holds promise as a transformative solution for individuals living with retinal degenerative diseases, offering renewed possibilities for enhanced vision and quality of life.

Ultra-high spatio-temporal resolution imaging with parallel acquisition-readout structured illumination microscopy (PAR-SIM)

Structured illumination microscopy (SIM) has emerged as a promising super-resolution fluorescence imaging technique, offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological specimens. Traditional efforts to enhance system frame rates have concentrated on processing algorithms, like rolling reconstruction or reduced frame reconstruction, or on investments in costly sCMOS cameras with accelerated row readout rates. In this article, we introduce an approach to elevate SIM frame rates and region of interest (ROI) coverage at the hardware level, without necessitating an upsurge in camera expenses or intricate algorithms. Here, parallel acquisition-readout SIM (PAR-SIM) achieves the highest imaging speed for fluorescence imaging at currently available detector sensitivity. By using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process, we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels · s−1, 9.6-fold that of the latest techniques, with the lowest SNR of −2.11 dB and 100 nm resolution. PAR-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations, even under conditions of low signal due to ultra-short exposure times. Notably, mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell, recorded with PAR-SIM at an impressive 408 Hz. We posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.

Skin Disease Detection and Remedial System

With the advent of smart processing systems in computers it has driven the emergence of inventive solutions within health-care. One notable instance is the Skin Disease Detection and Recommendation System, utilizing AI and machine learning methods to elevate dermatological diagnosis and treatment guidance. This summary offers a comprehensive overview of the Skin Disease Detection System, outlining its core elements, methodologies, advantages, and potential healthcare impact. The System for Skin Disease Detection aims to transform dermatology by automating the skin disease identification process and delivering customized treatment suggestions. This also aims to detect the skin types and suggest remedial medication and other things for the same. Consulting with a dermatologist is also easy by this. Employing image processing, pattern recognition, and deep learning algorithms, this system accurately evaluates skin condition images. The solution's application was developed using Streamlit, Python, PHP, Bootstrap, and MySQL.

Object Tracking Using Computer Vision: A Review

Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines.

Research on safety condition assessment methodology for single tower steel box girder suspension bridges over the sea based on improved AHP-fuzzy comprehensive evaluation

In order to propose a reliable method for assessing the safety condition for single-tower steel box girder Suspension bridges over the sea, a condition monitoring system is established by installing sensors on the bridge structure. The system is capable of gathering monitoring data that influence the safety status of the bridge. These include cable tension, load on the main tower and pylon, bearing displacement, wind direction, wind speed, and ambient temperature and humidity. Furthermore, an improved Analytic Hierarchy Process (AHP) algorithm is developed by integrating a hybrid triangular fuzzy number logic structure. This improvement, coupled with comprehensive fuzzy evaluation methods, improves the consistency, weight determination, and security evaluation capabilities of the AHP algorithm. Finally, taking the No.2 Channel Bridge as an example and based on the data collected by the health monitoring system, the application of the safety assessment method proposed in this paper provides favorable results in evaluating the overall safety status of the bridge in practical engineering applications. This provides a basis for management decisions by bridge maintenance departments. This project confirms that the research results can provide a reliable method for assessing the security status of relevant areas.

Integrated urban land cover analysis using deep learning and post‐classification correction

AbstractThe quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km2 (or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km2 (or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure.

Integrated Image Processing and Gas Sensing for Enhanced Hazard Detection.

In industrial and public safety applications, detecting hazardous situations in a timely manner is critical for averting accidents and maintaining human safety. This work offers a novel method for identifying dangerous environments by using cutting-edge image processing algorithms. Using sophisticated image processing algorithms, the technology extracts pertinent information from visual data that is recorded by cameras or other sensors. Gas sensors are simultaneously used to track the chemical composition of the surrounding air, with a special emphasis on chemicals that may pose a threat. The integration of gas and optical sensor data allows for a more thorough analysis, which enhances the system's precision in identifying and categorizing dangerous situations.

Web Application for Company Stock Price Prediction

This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

A novel sparse recovery space‐time adaptive processing algorithm using the log‐sum penalty to approximate the ℓ0 − norm penalty

AbstractApplying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods.

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method based on image processing algorithms for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.