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Seamless Interaction through Gesture Recognition: Integrating Virtual Canvas, Keyboard, Calculator And Mouse with Voice Assistance on a Unified Platform

Seamless Interaction through Gesture Recognition: Integratin...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Jimsha K Mathew D Yashas M Shivani Kashyap K Jyothsna K Vinay Prasad Pratibha Prakash Machakanur Sr. Assistant Professor Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Computer Science and Engineering New Horizon College of Engineering Bengaluru

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

This paper discusses the project ‘Seamless Interaction through Gesture Recognition,’ which is a system designed for the human-computer interaction without touching any tool, it simulates it virtually through three gadgets: a gesture-controlled mouse with voice recognition facilities, an air-based keyboard, calculator and a drawing canvas. In order to interpret the hand movements, gestures, and voice commands that are captured by the webcam and microphone, the system applies computer vision, machine learning, and speech recognition technologies. Hand motions made by the virtual mouse communicate to the computer what actions are to be done and where to locate the cursor. Additionally, the computer recognizes the voice commands which are an aide to such usage. The virtual keyboard, on its part, suggests finger positions through the air as keystrokes, virtual calculator processes a calculation based on hand gestures, and the virtual drawing tool is the one that notices the hand actions that it changes into digital artwork. All of these were developed by the OpenCV and MediaPipe frameworks, the programs that allow correct real-time hand tracking and gesture identification. The findings are a valuable contribution to computer interaction formation, which can lead to a decrease in the use of physical interaction devices and a shift to more natural interaction methods.

关键词： Hands Human computer interaction computer vision Calculators Webcams Keyboards Speech recognition Machine learning Gesture recognition Real-time systems

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Early Localized Lyme Disease prediction using Image Processing and Machine Learning Techniques

Early Localized Lyme Disease prediction using Image Processi...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Saravanan M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The purpose of this study is to predict the early Lyme disease with the set of images and applying few machine learning algorithms. For this dataset taken from the public medical image repositories such as DermNet NZ, HAPiD, etc., using this two Convolutional Neural Network (CNN) machine learning algorithms such as VGGNet and GoogLeNet. The dataset size predicted using gpower tool with eighty percent of sample setting. With the sample size prediction the CNN type of VGGNet and GoogLeNet both are iterated and the results are recorded using training and testing data. The results were such as accuracy, precision, recall, F1-score, and ROC-AUC are determined to predict early localized Lyme disease. It was found that the novel CNN VGGNet model achieved an accuracy of 96.84% and other performance metrics compared to the CNN GoogLeNet algorithm’s 91.34% for the Lyme disease dataset. Hence this research study can help the healthcare practitioners or experts to identify the early growth of Lyme also this study result accepted the statistical significant of p value of 0.001, which is less than 0.05 in the independent t-test on both the machine learning algorithms. Therefore, the results indicate that the CNN VGGNet machine learning algorithm proves useful for early Lyme disease prediction and is effective at recognizing skin lesions.

关键词： Measurement Training Machine learning algorithms Accuracy Image processing Machine learning Skin Convolutional neural networks Lesions Diseases

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A Review on Forensic Investigation Analysis in Cloud Computing Environments

A Review on Forensic Investigation Analysis in Cloud Computi...

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International Conference on Computational science and Technology (ICCST)

作者： R.Sharmikha Sree K. Raja Department of Computer Science and Engineering Sri Sairam Engineering College SRM University Assistant Professor Department of CSE SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (纸本)9781665476560

In cloud forensics, scientific concepts, methods, and approaches are used to find, collect, retain, evaluate, and report digital data in order to reorder events. For everyone involved, including cloud service providers (CSPs), cloud consumers, researchers, and forensic practitioners, cloud forensics is getting increasingly complex. If a criminal want to conceal their dirty laundry, they can use cloud storage to conceal sexual photographs of children, documents related to terrorist groups, etc. To investigate crimes involving the cloud, forensic investigations must be undertaken in the cloud. While attempting to collect evidence, forensic investigators face significant obstacles posed by the cloud's dynamic and dispersed nature. In this paper, Cloud-based forensics professionals are only able to conduct investigations if they have access to the proper tools and techniques for retrieving relevant digital evidence from a device is discussed. It further presents the challenges associated with cloud based forensics and applications to move forward the research in current area.

关键词： Cloud computing Scientific computing Forensics Terrorism Taxonomy Faces Logistics

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia

Texture-based Feature Extraction and Machine Learning Model ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Rangini M Sumit Pundir Manzoore Elahi M. Soudagar Daxa Vekariya Harshal Patil S. Rajkumar Assistant Professor Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable.

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Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

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Usability Evaluation of Kids’ Learning Apps

Usability Evaluation of Kids’ Learning Apps

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Amer Ibrahim Murad Al-Rajab Khalid Hamid Muhamamd Aqeel Salman Muneer Mehvish Parveen Maryam Saleem College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates Assistant Professor Computer Scienc and IT College of Engineering Abu Dhabi University Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Science NCBAE Lahore Pakistan

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids’ fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically.

关键词： Electronic learning User interfaces Security Usability Standards Business

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A Survey on Cyber Attack Detection: Techniques, Datasets, and Challenge

A Survey on Cyber Attack Detection: Techniques, Datasets, an...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G Geetha Manjula Rajagopal K Purna Chand Department of Computing Technologies School of Computing (Research Scholor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computing Technologies School of Computing (Assistant Professor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering(Data Science) (Professor) B V Raju Institute of Technology Narsapur India

Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.

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Handling tactful data in cloud using PKG encryption technique

Handling tactful data in cloud using PKG encryption techniqu...

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4th Smart Cities Symposium (SCS 2021)

作者： B.C. Gajarla A.V. Rebba K.S. Kakathota M. Kummari S. Shitharth Student Department of Computer science and EngineeringVardhaman College of Engineering Assistant Professor Department of Computer science and EngineeringVardhaman College of Engineering

With the availability of the cloud storage services, users can store data to the cloud and have the access to share the data to others. The cloud file may contain some sensitive information, this information shouldn't be exposed to others. The possible solution could be to encrypt the whole file which is shared before sending it to the cloud and to generate the signature to verify the encrypted file. Then, upload the encrypted file and signatures to the cloud. This enables the sensitive information to be private and only the owner can decrypt the file. Hence, it is not feasible to use by others. Distributing the decryption is the possible way to access it by others. However, it is not the efficient solution to implement this method in real scenarios. In order to overcome this approach, an efficient solution is implemented. In this new approach, sensitive information is kept private and can easily execute without any complications. Here, a sanitizer is used to sanitize the block which contains the sensitive information of the file. Firstly, the user binds the data blocks with respect to the sensitive information of the original data and generates a digital signature to that block and then sends it to the sanitizer. The sanitizer filters these data blocks and also sanitizes the data corresponding to the sensitive information. It also evaluates the corresponding signatures into valid ones. The signature is used to prove that the cloud truly possesses the data blocks. The third-party auditor is a public verifier, it verifies the data stored in the cloud on behalf of the user. This method supports the data sharing so that the sensitive information is protected in the cloud.

关键词： cryptography data integrity digital signatures storage management cloud computing

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security

IoT Based Human Biometric System Using Alcohol Level Detecti...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Anurag Sinha Nitish Kumar Amrita Ticku Vyom Modi Meghal Shah Kapil Shrivastava School of Computing and Information Sciences IGNOU New Delhi India Department of Computer Science Lovely Professional University Phagwara Punjab India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Rajkot Gujarat India Department of Computer Science & Engineering Charotar University of Science & Technology Gujarat India Assistant professor Department of computer engineering and applications GLA University mathura India

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver; they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol.

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