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Safeguarding Information in QR Codes Through Steganographic Technique

Safeguarding Information in QR Codes Through Steganographic ...

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Communication, Control and Intelligent Systems (CCIS)

作者： Jyotsana Bhardwaj Ruby Panwar Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The act of concealing data within a different message or object in order to avoid detection is referred to as steganography. Steganography can hide various types of digital content such as text, images, audio, and video. Upon arrival, the hidden information is subsequently exposed. Employing a form of steganography called QR Image Steganography, in which a secret message is encoded into a QR code and subsequently hidden within an image to provide effective and efficient security. To safeguard the secret communication, encoding is carried out on the sender's end, and decoding is done on the recipient's end. This paper is focused on the idea that, in order to maintain confidentiality and prevent a third party from spotting the hidden message in the stegano image, the cover image should not be significantly altered when concealing the QR code containing the secret message. Additionally, the PSNR values of the cover picture and the stegano image were used to verify this; they were fairly high, showing that the cover image had slight distortion. In order to determine the pixel value change where the distortion is not apparent, the sample histograms of the cover and stegano picture were also plotted.

关键词： Steganography Histograms PSNR Process control Memory QR codes Distortion Encoding Encryption Intelligent systems

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Improved Resource Allocation in 6G Networks Through Artificial Intelligence 2

Improved Resource Allocation in 6G Networks Through Artifici...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Rolly Kumar Sagar, Pramod Malhotra, Sonal Kulshreshtha, Kushagra Srm Institute of Science and Technology Computer Science & Engineering UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Computer Engineering & Applications Up Mathura India

ISBN: (纸本)9798350371055

This paper proposes a unique resource allocation technique for 6G networks the use of synthetic intelligence (AI). To assist the evolution of cellular networks, aid allocation mechanisms must be designed to successfully allocate network assets and assist the large wide variety of to be had offerings. Thus far, this has been implemented with the aid of traditional algorithms which possess an upper restrict on their talents. AI-based useful resource allocation offers superior adaptively and scalability to such algorithms, through gaining knowledge of from historic records and feedback from the person terminals. On this paper, an improved aid allocation algorithm primarily based on the integration of AI technology is proposed, underneath the perspective of optimizing both electricity performance and network overall performance. The proposed set of rules relies on a deep Q-learning framework, taking gain from deep neural networks to dynamically examine the most reliable resource allocation strategy. Simulation consequences display an improvement in network performance and energy performance in comparison to traditional optimization schemes. © 2024 IEEE.

关键词： dynamically mechanisms propose schemes technique

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Machine Learning based model to Detect Anomaly in the Water

Machine Learning based model to Detect Anomaly in the Water

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Kamred Udham Singh, Teekam Kumar, Ankit Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

The environmental impact of technology is only one area where the field is expanding rapidly without necessarily good results. [1] Water contamination is one such critical problem. Because water is essential to human survival, polluting it has serious consequences for civilization. [1] Knowing what harmful substances are in the water and being able to identify them is a crucial step toward reducing the damage they cause. While Image classification and sparse coding have been widely employed for water pollution identification [1,] they have unintended implications when applied to bacterial information detection. The current technologies used to identify contaminants in photos and water pipes, such as Convolutional neural networks (CNN), natural language processing (NLP), and the Internet of Things (IoT), are expensive and inefficient when compared to 3D microscopy data. It is a model that uses categorization and data visualization to estimate the total number of bacteria in a given environment. Accuracy scores may be seen in the model's studied graphs, decision tree, confusion matrix, and classification report. By the use of these data sets, the crucial factors for clean water were evaluated, and a model was built that can categorize the massive amounts of information, find meaningful patterns, and make reliable predictions. It has been determined which method yields the highest accuracy prediction (87%) by thoroughly analyzing the available models' accuracy scores. This study thus provides a valid evaluation of bacterial water vs unadulterated water. © 2023 IEEE.

关键词： Decision trees

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Instantaneous Interpretation into Sign Language for the Hearing Impaired 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Because they are unable to convey their thoughts to others who can hear, deaf persons may have a difficult time controlling their emotions and may find that their routines are often interrupted. This is the primary impetus behind the progression of things. These individuals would be helped by this system, and it would make it possible for them to convey their ideas in a manner that is on par with that of persons who do not have any kind of physical impairment. Recent advances in artificial intelligence have made it possible to design a system that is capable of resolving this problem, which was previously thought to be impossible. The purpose of this research is to develop, for the benefit of the deaf community, a system that can translate speech into text. Because it’s possible that some individuals won’t comprehend only the written word, the speech will also be translated into international sign language (ISL). In this method, persons who are deaf or hard of hearing will have their sign language translated into spoken language. In order to develop the most accurate model possible, we will integrate NLP with a variety of ML and AI techniques. For the purpose of prediction, convolutional neural networks (CNN) will be used since lip movements are continuous and rapid, making them challenging to capture. CNN and attention-based long and short-term memory (LSTM) approaches will function since they are both effective at predicting visual input. In order to get more desirable results, we will be using several techniques for augmenting the data. TensorFlow and Keras are going to be the Python libraries that are employed in order to do the voice-to-text translation. At the moment, there is an abundance of software accessible;however, all of them require the use of a network. This piece of hardware can function very well without being connected to the internet. Using the suggested technique, we were able to attain a 100% accuracy rate in sign language prediction and a 96% a

关键词： Forecasting

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A Novel Object Detection and Tracking Approach Using LSTM Networks and DSOD for UAV

A Novel Object Detection and Tracking Approach Using LSTM Ne...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ankit Kumar Kumar Sankar Ray Department of Computer Engineering & Applications GLA University Mathura UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Unmanned Aerial Vehicles (UAVs) are being used in traffic control, disaster response, and logistics. UAVs provide very detailed monitoring and object tracking, surpassing traditional surveillance systems in high-resolution data collecting and access to inaccessible regions. We develop a new object detection and tracking method utilizing LSTM networks and Deeply Supervised Object Detector in this paper. This method takes use of UAVs' mobility and high altitude. In dynamic environments, it improves object tracking. The technique relies on object labeling for precise position identifiers, Deeply Supervised Object Detector (DSOD) training on frames with improved object recognition, and LSTM for advanced processing of visual characteristics and bounding box coordinates from DSOD. Integrating the DSOD unit with the LSTM corrects the vanishing gradient problem, improving gradient propagation to capture crucial temporal correlations across frames. Occlusion, size and light variations, and camera motion are in the UAV123 dataset, which we used for our experiments. The recommended strategy outperforms all previous static camera data-based training methods in accuracy, recall, and F-scores, obtaining 97.42%, 96.13%, and 96.21%, respectively. This shows the need of training object detectors using UAV data. DSOD and LSTM provide a scalable and efficient framework for real-time object detection and tracking, improving UAV surveillance and autonomous systems. The findings suggest that deep learning methods may improve object recognition and tracking, particularly in complicated scenarios.

关键词： Training Deep learning Visualization Detectors Object detection Autonomous aerial vehicles Cameras Object recognition Object tracking Long short term memory

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Local and Global Features Based on Ear Recognition System

Local and Global Features Based on Ear Recognition System

引用

International Conference on Artificial Intelligence and Sustainable engineering, AISE 2020

作者： Agarwal, Rohit Department of Computer Engineering and Applications GLA University U.P. Mathura281406 India

ISBN: (纸本)9789811685453

Ear images is a promising physical quality that has been lately advanced as a biometric resource, inferable from its age-autonomous structure, its invariance to outward appearance, and its reasonable and notable structure. Human ear contain rich and stable highlights, it is more dependable than face on the grounds that the structure of the ear does not change in relative to increment in age. It has been discovered that no two ears are actually the equivalent even that of indistinguishable twins. The researchers proposed a few papers for human ear imaging. Unlike fingerprint and iris, ear-based biometric system does not need user cooperation to acquire samples and can be captured without acknowledgment of user in the unconstrained environment. In this paper, we extract the local Haralick features and NGTDM global features. Finally, these features are clubbed and form a final feature vector, which is passed to SVM for recognition. The ear recognition are briefly implemented on two publicly available ear datasets, namely IIT Delhi (I), IIT Delhi (II). The obtained results for recognition show that the extracted local and global features are successful for ear recognition system. The overall accuracy of the proposed system is 98.4% and error rate is 1.825. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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Potato Leaf Disease Detection Method is Based on the Support Vector Machines 2

Potato Leaf Disease Detection Method is Based on the Support...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Srivastava, Animesh Singh Rawat, Bhupender Sawan, Vikash Vishal Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India College of Smart Computing COER University Roorkee India GLA University Department of Computer Engineering & Applications Uttar Pradesh Mathura India School of Computer Application Lovely Professional University Punjab Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331519056

Potato production is challenged by plant diseases whose adverse effects are a reduction in the yield of the crops, and which have undesirable economic implications. Recognizing these diseases is of utmost importance since control should be applied. This paper describes a method for diagnosing diseases of the potato using Support Vector Machine (SVM) classification. A SVM is an ML algorithm that is effective in classification tasks such as classifying potato leaves in terms of their diseases. The results show that the proposed method is useful and works rather well for detecting potato plant leaf diseases. In this paper, the SVM method emphasizes detecting various potato plant leaf diseases for efficient intervention before the late stage of visual assessment for microbial-introduced disease. The main aim of this research is to assist in overcoming the limitations of existing agricultural practices in speeding up the diagnosis and control of diseases affecting potato plants. So the SVM-based methods can help farmers in making timely decisions, reducing economic losses, and increasing agricultural productivity. With linear SVM, the accuracy stands at 93.1%. Nevertheless, when non-linear SVM is employed, the accuracy rises to 95.6%. © 2024 IEEE.

关键词： Agricultural economics

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Facial Features based Recognition System using LBPH and AdaBoost

Facial Features based Recognition System using LBPH and AdaB...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Mayank Srivastava Kalash Khatiyan Pratibha Dixit Department of Computer Engineering & Applications GLA University Mathura UP India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Face recognition stands as a pivotal domain within computer vision, boasting myriad applications in security, identification, and biometrics. This research study proposes a face recognition system using image processing techniques with the combination of two algorithms, Local Binary Pattern Histogram (LBPH) and AdaBoost. The LBPH algorithm was used to extract features from the images and create a feature vector for each face. The AdaBoost algorithm was then used to train a classifier on the feature vectors and classify new faces. After testing on the Face Recognition Database, the results reveal that the fusion of LBPH and AdaBoost algorithms achieves an impressive accuracy rate of 96.15% which is better as compared to other similar algorithms. The proposed algorithm is implemented in Python to recognize faces in a dataset of images.

关键词： Histograms Accuracy Image recognition Law enforcement Face recognition Feature extraction Vectors Real-time systems Classification algorithms Testing

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Data Science - A Compendious Study on Statistical Methods and Visualization Techniques

Data Science - A Compendious Study on Statistical Methods an...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Singh, Kamred Udham Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Teekam Kumar, Gaurav Kumar, Ankit School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Knowledge and insights may be gleaned from both organized and unstructured data through the use of Data Science, which is itself a multidisciplinary discipline. Finding patterns and trends in data requires a wide range of methods, including but not limited to cleaning, transformation, analysis, and visualization. A data scientist's primary responsibility is to transform unstructured data into a form suitable for analysis through the processes of processing and cleaning. This approach utilizes several statistical procedures, such as calculating the mean, mode, median, standard deviation, correlation, regression, and hypothesis testing. These techniques are useful for discovering abnormalities, missing values, and outliers in data, as well as for eliminating them. When the data has been cleansed and organized, it will be analyzed using statistical and machine-learning techniques to draw conclusions and make forecasts. To find patterns, trends, and correlations in the data, this study may make use of methods including clustering, classification, regression, and time series analysis. Data visualization is crucial to data science because it facilitates the dissemination of ideas and conclusions to stakeholders who may not have technical expertise. Scatter plots, bar charts, heat maps, and network diagrams are just a few of the data visualization methods that may be utilized to convey information interestingly. A wide variety of fields, from economics and finance to medicine and social media, can benefit from data science. Data science is useful in business for several purposes, including cost reduction, client retention, and discovery of untapped markets. As a field of study, data science has the potential to improve healthcare by aiding in areas such as disease detection, treatment, and the creation of new medications. Data science may be applied to social media to study user behavior and sentiment and to spot and stop criminality. It is a dynamic and promising new area

关键词： Python

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Increasing Productivity in Software Development Through the Use of Docker Technology 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Information Technology Guru Ghashi Das University Bilaspur Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The Docker technology makes the process of developing software substantially more effective by resolving issues related to deployment and hosting. Through the use of virtualization, it is able to function independently of the underlying hardware infrastructure, and it relies on its own code to carry out the execution of programs. The encapsulation of an application's whole environment together with its source code is at the heart of Docker's methodology. This enables Docker to efficiently eliminate the problems that are associated with having a variety of working environments, but it may also result in the images having a greater file size. "Images" is the term that Docker uses to refer to these bundled environments. Docker was used in this investigation to carry out a variety of tests, which allowed identical code to be executed on a variety of platforms, so permitting extensive comparisons. Software developers made use of Docker's ability to analyze the outcomes, which enabled them to make decisions based on accurate information. This study simplifies the software development and deployment procedures for the development community by providing significant insights into the benefits and considerations of Docker across numerous platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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