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检索条件"机构=Computer Science and Engineering Ajay Kumar Garg Engineering College"

共 413 条记录，以下是61-70 订阅

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Multi-band Micro-strip Patch Antenna for C/X/Ku/K-Band Applications 6th

Multi-band Micro-strip Patch Antenna for C/X/Ku/K-Band Appli...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Srivastava, Karunesh Kulshreshtha, Mayuri Gupta, Sanskar Shukla, Shrasti Sanjay Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College UP Ghaziabad201009 India Department of Computer Science & Engineering IMS Engineering College Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9789819984503

In this paper, a multi-band E-shaped antenna is presented for C, X, Ku, and K-band applications. Proposed antenna consists of E-shaped radiating patch and defected ground plane. Introduction of partial ground plane in the design causes decrement in surface waves and increment in antenna gain. The proposed multi-band micro-strip patch antenna has been simulated and optimized using ANSYS HFSS version 15. The proposed antenna uses the FR4 substrate with thickness 1.6 mm, dielectric constant (Ɛ) 4.4, and loss of tangent (tan δ) 0.02. Micro-strip feed line (λ0/4 mm) with 50 Ω characteristics impedance is used for excitation of proposed antenna. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Kurchatovium

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Security System for Services Access in Smart City System Using Internet of Everything and Cloud 2

Security System for Services Access in Smart City System Usi...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Wadhwa, Amit Arora, Neerja G. L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350371055

With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture of Internet of everything. In a smart city the distinguish concept is the utilization of internet of everything architecture to effectively manage the services provided to users. Moreover, integration of internet of everything with cloud computing architecture would provide a better ecosystem in the area of smart cities. Providing services to smart city users could pose certain challenges and security issues with the integration of cloud computing. As per previous studied literature there is scope for improving access and authentication-based security of services imparted to smart city users. In this work, initially the consolidation of internet of everything (IoE) and cloud computing in smart cities is put forward. Further, this work focusses upon issues and challenges encountered in leveraging IoE in smart city environments with analysis of existing techniques for authentication and access control of cloud-based services and later provides a proposed system focusing upon imparting access control and authentication-oriented security for services delivered over a cloud and IoE followed by its analysis. © 2024 IEEE.

关键词： Access Control Authentication Cloud Computing Services IOE (Internet of Everything) Smart Cities

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The smart power optimization for sensor based wearable IoT devices using AI based deep learning 2

The smart power optimization for sensor based wearable IoT d...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： kumar, Anuj Sharma, Jyoti Goel, Mukulit kumar, Manish Chaudhary, Vikas Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Delhi NCR Ghaziabad India KIET Group of Institutions Delhi NCR Ghaziabad Department of Information Technology Delhi NCR Ghaziabad India Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Delhi NCR Ghaziabad India ABESEC College Ghaziabad Department of Computer Science and Engineering Delhi NCR Ghaziabad India

ISBN: (纸本)9798350368109

The intelligent power optimization for sensor-based wearable IoT devices with AI-based deep learning reduces the energy consumption of sensor-based wearable devices in IOT applications. The process employs deep learning algorithms based on artificial intelligence (AI) that can optimize the power consumption in such devices, making economical use of the battery and reducing frequent charging. Wearable IoT devices have long been a power-hungry type of wearable because they always need to sense and transmit data. This causes the battery life to shorten and your battery maintenance costs to increase. But at the same time, using deep learning-based algorithms of AI, they can understand and learn from their user's behavior so that it could save up to 40% power. This is done by real-time sensor data analysis and discovering user patterns, communication styles, and activity levels. This optimization process can actively change the power of different sensors and communication modules by identifying many patterns. This helps cut down power consumption and improves the overall performance of wearable IoT. In addition, energy harvesting from ambient sources such as body heat can be possible using this approach to extend battery lifetime even longer. © 2024 IEEE.

关键词： Smart sensors

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Malaria Disease Prediction Using Frequency-Based Machine Learning Algorithms and Ensembles Algorithms 2

Malaria Disease Prediction Using Frequency-Based Machine Lea...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ahmad, Hamisu Ismail Prasad, Rajesh Sharma, Birendra kumar Madaki, Aisha Yakubu Shuaibu, Aliyu Rabiu Nile University of Nigeria Department of Computer Science Abuja Nigeria Ajay Kumar Garg Engineering College Department of Computer Science and Engineering UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Mca UP Ghaziabad India Nile University of Nigeria Department of Software Engineering Abuja Nigeria

ISBN: (纸本)9798350358834

Malaria is one of the most serious and threatening diseases in Sub-Saharan Africa. Its cases increase drastically during the rainy season. It can spread throughout an infected person's entire body in less than an hour. The diagnosis process is time-consuming, and its accuracy is negatively affected due to the lack of technical tools and infrastructure in many laboratories in malaria-prone countries. Hospitals, however, keep patient data during the diagnosis period. Due to the vast amount of diagnostic data gathered and stored on a daily basis in various mediums such as files, it has become increasingly crucial to develop powerful techniques for analysing and interpreting the data. This analysis aims to extract meaningful knowledge and insights that can significantly contribute to malaria diagnosis and decision-making processes. This paper selected four frequency-based algorithms, namely Naïve Bayes, (J48) Decision Tree, ZeroR, and the OneR algorithm, to develop a hybrid model for frequency-based classification algorithms using the available dataset of malaria diagnoses collected from the Federal Medical Centre in Yola, Adamawa state. The research results indicate that the highest accuracy, 88.4%, was achieved by the (J48) Decision Tree model. Additionally, this research employed ensemble methods to enhance the performance of each classification model. The results demonstrated that the accuracy of the Decision Tree and ZeroR models remained the same at 88.4% and 60.9% before and after boosting, respectively. In contrast, the accuracy of the Naïve Bayes and OneR models increased from 79.9% to 87.0% and from 79.8% to 87.6% before and after boosting, respectively. In conclusion, among the frequency-based classification models, the Decision Tree model consistently exhibited the highest accuracy both before and after boosting. © 2023 IEEE.

关键词： Decision trees

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IOT Based Plant Growth Monitoring and Ideal Condition Detection System

IOT Based Plant Growth Monitoring and Ideal Condition Detect...

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Disruptive Technologies (ICDT), International Conference on

作者： Bidur Gupta Abhinav Singh Akash Bansal Anik Agarwal Rekha Baghel Computer Science Ajay Kumar Garg Engineering College Ghaziabad India Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent times, the Internet of Things (IoT) has inched itself as an evolutionary technology in agriculture as it allows for managing and controlling the growth conditions of the plants in an optimal way. This paper demonstrates the features of an IoT based system for effective plant growth monitoring along with ideal condition detection. The set of sensors embedded in the system incorporates measuring devices that capture and send the temperature, humidity, soil moisture content and light intensity to a central processing unit. The system sets and analyzes the data cloud along with AI algorithms for efficient agricultural activities and systems. This study focuses on the system design, its data gathering procedures, and decision processes, and their potential in smart farming and urban gardening. The experiments, conducted within the framework of the given concept showed that the system was able to operate within ideal conditions and therefore when changes occur, it initiates an action that helps avoid resource losses and assist in improving the yield. It reiterates the importance of IoT in world of agriculture and the quest for sustainability in modern farming practices.

关键词： Temperature sensors Temperature measurement Smart agriculture Accuracy Predictive models Sensor systems Internet of Things Reliability Sustainable development Monitoring

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Corner Truncated Elliptical Slot Fractal Antenna for Wireless Applications 7th

Corner Truncated Elliptical Slot Fractal Antenna for Wireles...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Rajput, Sonia Kulshreshtha, Mayuri Srivastava, Karunesh Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad201009 India Department of Computer Science and Engineering IMS Engineering College U.P. Ghaziabad201009 India

ISBN: (纸本)9789819609239

In this work, ultra-wideband corner truncated elliptical slot fractal-shaped antenna has been designed and simulated for fixed amateur, fixed mobile, aeronautical mobile, broadcasting, maritime mobile, fixed radio astronomy, amateur-satellite, standard frequency, and time signal applications. The design evolved in four different steps. UWB is achieved by carving a notch in the upper part of the radiating patch with a partial ground structure. FR4 substrate having height of 1.6 mm is used for designing the antenna. The area of the proposed antenna is 1085 mm2 with sufficient gain in the resonating band (3.3–15 GHz). The proposed antenna sought applications in various wireless communications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultra-wideband (UWB)

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Review on Early Prediction of Body Dysmorphic Disorder Using Machine Learning 2

Review on Early Prediction of Body Dysmorphic Disorder Using...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Saini, Harnit Prasad, Sanjeev kumar School of Computing Science of Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350305418

Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by excessive preoccupation with perceived defects in one's appearance, often leading to distress and functional impairment. Early prediction of BDD is crucial for timely intervention and improved treatment outcomes. In recent years, machine learning techniques have shown promise in identifying individuals at risk of developing BDD based on various data sources, including facial images, clinical information, and textual data. This review aims to provide an overview of the current state of research on the early prediction of BDD using machine learning, highlighting the methodologies, challenges, and future directions in this emerging field. We conducted a comprehensive literature search and identified relevant studies that employed machine learning algorithms for BDD prediction. The reviewed studies demonstrated the effectiveness of machine learning techniques such as support vector machines, deep learning models, ensemble learning, natural language processing, feature selection and extraction, and clustering in predicting BDD. Despite the promising results, several challenges need to be addressed, including the availability of large and diverse datasets, standardization of evaluation metrics, and interpretability of the models. Future research should focus on refining the existing approaches, integrating multiple data modalities, and developing user-friendly tools for clinical practice. Early prediction of BDD using machine learning holds great potential for improving the identification and intervention of individuals at risk, ultimately leading to better outcomes and quality of life for individuals with BDD. © 2023 IEEE.

关键词： Body Dysmorphic Disorder clustering deep learning early prediction ensemble learning feature selection machine learning natural language processing support vector machines

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Contrast Enhancement and Noise Reduction of chest X-ray images 1

Contrast Enhancement and Noise Reduction of chest X-ray imag...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Sharma, Megha kumar, Dharmendra Ajay Kumar Garg Engineering College Department of Computer Science Engineering Ghaziabad India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

X-ray radiography is used to get medical images of body parts such as chest, bones etc. These images help in detection of anomaly in inspected body part, for eg- Chest X-ray are used for detection of many diseases such as Covid-19, Pneumonia and Cancer. However, images obtained from radiography are low in contrast and with high noise level. Enhancement of an image is very crucial for the diagnostic purpose, as currently medical images are very helpful in identifying various disease and problem in human body. With the technical support, the enhancement is considered one of the first-rate methods for the betterment of visualization and raising the standard for understanding and clearing the image details. In our work, we have focused on the contrast enhancement and noise reduction, using Histogram equalization, CLAHE (Contrast Limited Adaptive Histogram Equalization), median filter and DCT filter for chest X-ray images of COVID-19 positive patients. The dataset of 6,334 images are collected from the Kaggle repository. All these methods are combined and as a result, has provided the best output by giving a colored enhanced image, highlighting the major details. This work will be helpful in the diagnosis of various kind of the diseases from radiographic approach. In the future, we will extend the process for the diagnostic part of COVID-19 from the enhanced images dataset, which will help in easy detection and work as a technological support to healthcare system. © 2022 IEEE.

关键词： Noise abatement

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Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images 1

Comparative Analysis of Image Enhancement Techniques for Che...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Sharma, Megha kumar, Dharmendra Ajay Kumar Garg Engineering College Department of Computer Science Engineering Ghaziabad India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images. © 2022 IEEE.

关键词： Noise abatement

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Efficient Liver and Tumor Segmentation from CT Images Using a Hybrid ResUNet Model 3

Efficient Liver and Tumor Segmentation from CT Images Using ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Aditya Pratap Mishra, Santosh kumar Nidhi Singh, Jagendra Agrawal, Madhup Joshi, Prakash Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350364569

In this paper, we investigated deep learning models for automated segmentation of liver and tumor regions from CT images to improve the diagnostic accu-racy as well as treatment planning in case of liver cancer. The research used a dataset of 3200 CT scans, with about 70% serving as the testing set and 30% for training. Preprocessing steps like Auto Windowing HU (Hounsfield Units) and Histogram Equalization to retain better clarity of the images. Based on the metrics of performance such as accuracy, Dice coefficient, IoU(Intersection over Union), precision and recall were calculated used three types of CNN models (ResUNet, VGG16 and VGG19). Its hybrid architecture combining U-Net and ResNet, called as ResUNet indicated better results with an accuracy of 97.3%, against VGG16 at 93.5% and VGG19 at 91.5%. The model also demonstrated good data loss reduction from 0.35 to about 0.10 over the course of a total for around an epoch angle in enduring caching past phase. VGG16 and VGG19 outperformed the baseline performances with reduced data losses while higher accuracies, although their convergence rate was slightly slower than ResUNet. The results confirmed the high precision of liver/tumor segmentation that ResUNet provides, which would be useful for clinical practice to achieve sufficient diagnostic improvement. The architecture of ResUNet specifically designed for the segmentation tasks in medical imaging from this study, demonstrates to be a versatile framework favoring its potential integration into automated tools within oncological diagnostics and could thus radically transform liver cancer detection. © 2024 IEEE.

关键词： computerized tomography

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