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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： v Alekhya N v Uma Reddy Jay Singh Bhasker Boddu Rajeev Sobti Ali Abduhussien Hameed Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India GL Bajaj Institute of Technology & Management Greater Noida U.P. India Department of Information Technology MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This research study explores the emerging area of quantum-inspired evolutionary algorithms (QIEAs) applied to high-dimensional data processing, with a focus on homeland security imaging systems. This work attempts to close the paradigm gap in image processing methodologies caused by the growing complexity of security threats. By combining the adaptive processes of evolutionary algorithms with the probabilistic reasoning inherent in quantum computing, the proposed technique creates a potent tool for managing the inherent difficulties of high-dimensional data. The main contribution of this study is the creation of a brand-new QIEA framework that is specifically designed for image analysis in security contexts and exhibits higher accuracy and efficiency than traditional techniques. The quantum bit representation and quantum gate operations, which have been cleverly tailored to the evolutionary algorithm structure, provide the methodological basis, and improve the search capacity in multidimensional data fields. An unparalleled degree of clarity and detail in security photography is made possible by this fusion, which is essential for threat identification and prevention. Experiments conducted on a variety of difficult datasets show that the suggested method is reliable for identifying important characteristics in complicated photos, which is a crucial component of homeland security applications. Beyond only addressing short-term security issues, this study establishes a standard for next investigations into quantum-inspired computing for image processing.

关键词： Quantum computing Accuracy US Department of Homeland Security Evolutionary computation Data processing Feature extraction Complexity theory

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Interactive Sign Language Learning System using Deep Learning

Interactive Sign Language Learning System using Deep Learnin...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： S Ravikumar P. Dimple Praharsha J. Sri Harsha Priya v. Naga Rohit Sai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The “Interactive Sign Language Learning System” is a sophisticated application designed to facilitate the learning process of sign language learners. This comprehensive system encompasses several key features, including sign language alphabet and word recognition, text-to-action conversion for learners, multi-language support, and integrated voice output functionality. The system utilizes advanced algorithms for sign language recognition, employing techniques such as image processing and machine learning to accurately interpret hand gestures and movements. For sign language alphabet and word recognition, a combination of computer vision algorithms, possibly including convolutional neural networks (CNNs) or recurrent neural networks (RNNs), may be employed to analyze input images or video streams and classify them into corresponding sign language symbols or words. Text-to-action conversion involves mapping textual input to corresponding sign language actions or gestures, possibly using natural language processing (NLP) algorithms to understand the semantics of the text and generate appropriate sign language representations. The system’s accuracy, measured in terms of correctly recognized sign language symbols or words, would depend on the effectiveness of the algorithms employed and the quality of the training data. With rigorous development and training, the system aims to achieve high accuracy levels, in sign language recognition and text-to-action conversion tasks.

关键词： Sign language Accuracy Machine learning algorithms image recognition Symbols Training data Speech recognition Natural language processing Classification algorithms Convolutional neural networks

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FPGA based Smart and Sustainable Agriculture 36

FPGA based Smart and Sustainable Agriculture

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36th International Conference on vLSI Design (vLSID) / 22nd International Conference on Embedded systems (ES)

作者： Peraka, Shyam Ali, S. K. Irfan Mogili, Durga vasu Palivela, Ashok Kumar Reddy, Sudheer Bavisetti, Jyotsna Reddy, Dhanush Y. RGUKT Nuzvid Dept Elect & Commun Engn Nuzvid India RGUKT Nuzvid Dept Comp Sci & Engn Nuzvid India

ISBN: (纸本)9798350346787

The agriculture sector is one of the key areas of concern interlinked with most of the Sustainable Development Goals (SDGs), as stated by most organizations. However, technological advancements and agricultural intervention, particularly in developing and underdeveloped countries, are still in their early stages. IoT, cloud computing, data analysis, and other technologies are being widely used in most areas, where they are bringing significant changes in outcomes compared to previous conventional practices. It is also observed that there have been some initiations in bringing such technologies into agriculture to assist farmers, but they are not comprehensive and real-time working prototypes. Hence, we have come up with a novel, comprehensive solution to address different issues. The focus of the proposed paper is to develop and design a real-time prototype model implementing the following: 1. Control of the irrigation system based on crop stage requirements and other factors such as soil nature, humidity levels, rainfall levels, and so on. 2. Detection of weeds and disease using image processing and machine learning algorithms 3. Identification and recommendation of suitable crops based upon the nature of the soil, fertility levels, climatic conditions, water availability, etc. 4. voice-based responsive system-mobile application or web-based application. The above tasks were successfully implemented and tested on the prototype developed with the help of the DE10 Nano Cyclone FPGA board. The sensors used in the project include the NPK sensor, moisture sensor, pH sensor, electric conductivity sensor, and water level sensor. Azure cloud services are used for the deployment of the website. The farmer is able to get the voice-based response through a mobile application developed for this purpose.

关键词： DE10-Nano Cyclone v FPGA Azure Cloud Sensors Crop Recommendation Smart Agriculture Sustainable Agriculture Soil Testing Smart Irrigation Weed Detection Disease Detection voice commands Web Application Machine Learning algorithms Convolution Neural Networks Yolo v5 XG Boost

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High-speed recursive-separable image processing filters with variable scanning aperture sizes

COMPUTER OPTICS

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COMPUTER OPTICS 2023年第4期47卷 605-613页

作者： Kamenskiy, A. v. Kuryachiy, M. I. Krasnoperova, A. S. Ilyin, Yu. v. Akaeva, T. M. Boyarkin, S. E. FSBEO HE Tomsk State Univ Control Syst & Radioelec Lenin Ave 40 Tomsk 634050 Russia

In the process of development of computer technologies, the number of areas of their applica-tion naturally grows and, along with it, the complexity of the tasks to be solved, which entails the need for new research. Similar tasks include digital filtering of images in the field of medical tech-nologies and active-pulse television measuring systems. There are many methods and algorithms of digital filtering designed to solve the problem of improving the quality;algorithms that can im-prove the quality of images while reducing computational costs are widely used. High demands, which are made due to the constant growth in the size of the generated images, as well as the re-quirement for modern television systems, is real-time operation. When solving practical problems, it is required to use different filter aperture sizes, which provide an increase in quality and preser-vation of image details. The solution of these problems was the reason for the emergence of adap-tive filters that are able to change the parameters in the process of processing the received data, while not spending additional time on processing with an increase in the size of the aperture. The paper presents the principles of constructing adaptive image processing filters, which, by obtaining an input parameter indicating the required dimension of a multi-element aperture, are able to im-plement the construction of the required aperture. The Laplacian "Truncated Pyramid" filter and the "double pyramid" Laplacian were modified. A feature of these filters is the oddness of the multi-element aperture, so the coefficient used to build the mask is always set to odd. When using these filters, it is possible to use two coefficients that are responsible for increasing the filtration efficiency, since, in their original form, the Laplacian filters have a sum of coefficients equal to ze-ro. The experiment shows a comparison with high-dimensional filters that work when using classi-cal two-dimensional convolutio

关键词： image processing recursively separable filters multi-element apertures reduction of computational costs machine vision medical optics and biotechnology vision systems in difficult weather conditions

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Wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

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Digital Signal processing and its Applications (DSPA), International Conference on

作者： Sinan v. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms Signal processing algorithms Electrocardiography Wavelet analysis Distortion

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Autonomous drones based forest surveillance using Faster R-CNN

Autonomous drones based forest surveillance using Faster R-C...

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2022 International Conference on Electronics and Renewable systems, ICEARS 2022

作者： Chandana, v. Sri vasavi, S. Vr Siddhartha Engineering College India Department of Computer Science Engineering Vijayawada India

ISBN: (纸本)9781665484251

Unmanned Aerial vehicles (UAvs), often called drones, are airships that do not involve a human pilot or can carry any passengers. UAvs are an element of the Unmanned Aircraft System (UAS). They are accommodated with a ground station and a system to facilitate communication with the drone. UAvs can be controlled by a human operator using a remote control, as remotely-piloted aircraft (RPA), or with differing degrees of independence, from autopilot assistance to fully independent aircraft that does not require human interference. Lately, UAvs have been made capable of flying beyond visual line of sight (BvLOS), due to which autonomous drones have been used in various areas such as commercial, warfare, aerial photography, agriculture and forestry, and law enforcement. Autonomous drones are being used to perform aerial surveillance of forest areas to detect and locate forest fires. various deep learning algorithms are implemented along with image processing techniques to create a robust mechanism for detecting fire and smoke from the images or video frames obtained from the UAvs. This paper studies and proposes how image processing models and techniques can be incorporated into Unmanned Aerial vehicle systems to detect smoke and forest fires. © 2022 IEEE.

关键词： Drones

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FPGA Based image processing: A Comparative Study of Software-Centric and Hardware-Accelerated Approaches

FPGA Based Image Processing: A Comparative Study of Software...

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Smart Electronics and Communication systems (ISENSE), International Conference on

作者： P N Parameswaran S Janardan Siddharth R Nair Thejaswitha T K Nikhil M Adersh v R Dept of Electronics and Communication College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work compares two different approaches to image processing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardware-Accelerated image processing (HAIP), Software-Centric image processing (SCIP), and an in-depth analysis of their corresponding performance metrics. image processing algorithms were implemented in the SCIP phase, exclusively using the Zynq processing System (PS). On the other hand, the HAIP phase comprised creating distinct Intellectual Properties (IPs) for image processing tasks and integrating them with the Zynq PS through Direct Memory Access (DMA) and the Advanced eXtensible Interface (AXI) *** all assessed techniques, HAIP considerably outperformed SCIP in terms of processing time, for different image sizes, across both Zynq Zybo and Zedboard. On the other hand, as SCIP is software-centric, it showed minimal resource utilization and lower power consumption than HAIP, which showed higher resource utilization and power consumption. However, both methods produced processed images that were similar, highlighting the stability and dependability of FPGA-based image processing implementations. A comparative analysis between Zynq Zybo and Zedboard reveals insights into how different FPGA architectures impact the performance and resource utilization of image processing implementations. This work offers insights into the trade-offs between hardware-accelerated and software-centric approaches to image processing on FPGA platforms, guiding future design decisions for practical uses.

关键词： Hands Measurement Power demand image processing Intellectual property Logic gates Stability analysis Robustness Resource management Field programmable gate arrays

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Automated Pest Detection in Crop Fields Using Deep Learning Techniques

Automated Pest Detection in Crop Fields Using Deep Learning ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Premalatha, R. Somasundaram, R. Atmakuri, Prashant Sreeja, G.G. Raj, Pooja P. Kumar, Loveleen Cms College of Science and Commerce Department of Microbiology Tamil Nadu Coimbatore India Kongu Engineering College Department of Management Studies Perundurai Tamil Nadu Erode638060 India Prasad V Potluri Siddhartha Institute of Technology Department of Cse Andhra Pradesh Vijayawada India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamil Nadu Coimbatore641202 India Dr. C.V Raman University Department of Cse C.G Bilaspur India Swami Keshvanand Institute of Technology Management & Gramothan Department of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331543617

When it comes to discovering and eliminating pests, traditional methods are typically laborious, time-consuming, and hazardous to the environment. In recent years, deep literacy methods have emerged as interesting and potentially useful technologies for automating the finding of pests in agricultural fields. The purpose of this work is to provide a detailed evaluation of the operation of deep literacy methods in automated pest discovery. These methods include a variety of characteristics such as image processing, point birth, and bracket algorithms. In this paper, we discuss the difficulties and opportunities that are involved with the implementation of deep literacy-based pest discovery systems. These problems and opportunities include dataset reflection, model training, and scalability. In addition to this, we highlight recent developments, case stories, and new exploration directions in this rapidly developing field. Automated pest discovery systems have the potential to enhance agricultural operations by utilizing the powers of deep literacy. This would allow for the prompt and precise identification of pests, which would be necessary for the development of effective pest management techniques. Cutting-edge technology Within the scope of this investigation, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 were utilized for detection. For object detection, each of these models was selected. images have been marked with Yolo. Yolov8 outperformed previous trials by having the highest mAP of 84.7% and an average loss of 0.7939 for the detection of tiny pests. An Android application that offered real-time insect detection utilized the Yolov8 paradigm. The purpose of this research is to develop and deploy novel deep-learning models, analytical tools, and a procedure for identifying crop pests for pest management. © 2024 IEEE.

关键词： Adversarial machine learning

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Basal Cell Carcinoma Prediction in Pigmented Skin Infection using Intelligent Techniques 3

Basal Cell Carcinoma Prediction in Pigmented Skin Infection ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Siva Prasad Reddy, K.v. Archana, K.S. Department of Computer Science and Engineering Pallavaram Chennai India

ISBN: (纸本)9781665462167

Melanoma is considered as a most lethal form of cancer. Design and development of computer-aided intelligent algorithms for early detection of skin cancer is the emerging research area. Despite many conventional mechanisms, a new type of cancer caused by unrepaired Deoxyribonucleic acid (DNA) within the skin cells. Due to its nature of rapid genetic mutations on the skin, it widely affects other body parts if not treated at early stages of intelligent computing evidenced the development of automated medical diagnosis and recommendation systems. It is possible to identify between melanoma and other classification of skin cancer based on the symmetry, color, size, form, and other characteristics of lesions. Numerous efforts are made by many researchers to develop various deep learning and machine learning inspired classification and segmentation algorithms to analyses skin lesion images. In existing the algorithm used for this research was naïve bayes, support vector machine etc. Here, after several methods such as data pre-processing, image segmentation, feature extraction and the feature extraction and the proposed algorithm of adaboost method, which is used to tune the algorithm to predict the skin infection. Finally, the proposed model has achieved 92.5% accuracy when compared with existing work. © 2023 IEEE.

关键词： Feature extraction

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An Analytical Investigation on images and Application of Different Methodologies Presenting Significant Challenges in Attaining Reliable Categorization 15th

An Analytical Investigation on Images and Application of Di...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Laxmaiah, Bagam Bhukya, Shankar Nayak Narasimha, v. Rao, K. venkateswara Mrunalini, G. Balakrishna, Balineni CMR Technical Campus Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India

ISBN: (纸本)9783031889912

The accelerated progression of computer applications, transitioning from basic data processing to the realm of machine learning, can be attributed to the increased accessibility and abundance of extensive data sets obtained from various sensors and the internet. This study provides evidence that robots have the capacity to acquire self-awareness and evolve through appropriate teaching. The effectiveness of human categorization of photos is contingent upon the utilization of image analysis techniques and the implementation of diverse approaches, which provide inherent challenges. Due to the intricate nature of the task at hand, it has become imperative to implement automation techniques in order to achieve a significant level of precision. The proposed study aims to provide a comparative analysis on the efficacy and accuracy of a number of machine learning algorithms in the context of image categorization. The logistic regression approach, Naive Bayes classifier approach, Support vector approach, and Random Forest algorithms for classifiers were employed to evaluate the UC Merced dataset. The experimentation began by preprocessing and training of the dataset, which was then followed by the testing phase. At this juncture, the inquiry shifts its focus towards determining the optimal selection algorithm by analyzing the computed accuracies of the evaluated methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Random forests

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