Updated Home Page
A Brief Introduction
The goal is to publish articles & research that are cutting-edge in all fields of Engineering that is used in Current Technology & Innovation. The Journal’s editorial board will assess all papers submitted. Regular papers, short papers, review pieces, and other types of submissions were welcomed by the journal. All papers must present original, previously unpublished research with experimental or theoretical results. Articles should meet these requirements and should not be under consideration for publication elsewhere. Manuscripts must adhere to the journal’s style and are subject to both review and editing. If the technical committee finds any difficulties with the content or any violations of our publishing regulations, they reserve the right to reject the paper or ask for modifications. It is the prime responsibility of the author to gain proper authorization for the use of any copyrighted materials in the manuscript, prior to the submission of the manuscript to NIETJET.
Recent Published Articles
Mr. Shakti Sharma, Mr. Rishabh Sharma, Ms. Alka Singh
Why the High Attrition Rate for Computer Science Students: Some Thoughts and Observations
This paper is based on investigation of the possible causes for high attrition rates for Computer Science students. It is a serious problem in colleges/universities that must be addressed if the need for technologically competent professionals is to be met.
Km Mahak Jain, Mr. Sumit Modi, Ms. Namita Sharma
A Classification of COVID-19 Cases Using Fine -Tune Different Model of Machine Learning
The Machine Learning Techniques are used for finding upcoming or futuristic Prediction. It’s very powerful tools in finding of future prediction. From Last decade to recent days Data mining and Machine Learning play vital roles in many Industries. In last 2-3 Years overall World is suffering from a new Virus i.e., COVID-19. The first covid case was detected in Month of December. In very less time it spread out throughout the world. In 2-3 months, this illness is declared as Pandemic by WHO (World Health Organization). The behaviour of spreading of COVID-19 was very abnormal. it was growing Exponentially. Here in this Paper as a Researcher’s we are trying to find out overall Analysis of COVID-19. Here our main motto to finding How many patients gets infected, how many of them get cure in sometimes & finally How many of them goes to death. Here our main concentration is to finding the features of the COVID. We will be applying Machine Learning Algorithms like LR, SVM, DT and many more. After applying the above methods, we will find the performance measures of each one and compare with their values. Finally, we will try to Implement some new Enhancement in Existing Algorithms.
Md. Adieb Siddique, Md. Faiz, Ms. Roshan Kumari
A Survey Paper Based on Impact of IoT in Health Care Sector
The Internet of Things (IOT) is a sophisticated network architecture for communication, travel, and technology. The implementation of remote health monitoring and emergency notification systems is possible with IOT smart devices. IOT has a significant role in smart healthcare system. The policies and procedures that are highlighted in the healthcare system assistance to the scientists, researchers, and specialists that create smart devices, an advancement of current technology. This survey report explains how IOT interacts with several systems, including smart healthcare, which is a widely used system. The surveillance in the healthcare system suggested the use of smart items and technologies to reduce the inefficiencies of the current healthcare system. IOT-based healthcare utilizes advanced technology not seen in traditional healthcare.
Mr. Sagar Jaiswal, Ms. Shagun Upreti, Ms. Priyanshi Sharma
Deep Residual Learning for Image Recognition
Training deeper neural structures is more complex. To make it easier to train nets that are far deeper than those that have been utilised in the past, we provide a residual learning method. Rather than merely We explicitly again formulate the layers as the learning residual functions with regard to the layered inputs by learning non referenced functions. We give significant empirical evidences about these residual networks that may be optimised more easily and that significant depth increases might boost accuracy. We assess residual networks on the ImageNet dataset who have up to 152 layers—eight times deepthan VGG nets [40]—while keeping a less complex degree of complexity. On the ImageNet examination set, a mixture of these residual the nets achieve a margin of error of 3.57%. With this outcome, the ILSVRC for 2015 classifying job was won first place.Furthermore, we present a study of CIFAR 10, which with layers (100 and 1000.Numerous tasks involving visual cues placed a premium on illustration depth. Our exceptionally deep
representations are the sole explanation for how we achieve a 28% relative enhancement on the COCO object detection dataset. Our contributions to the theILSVRC & COCO 2014 matches1, where we also took the honours for ImageNet detection, ImageNet localization, the COCO data set detection, and COCO
splitting, are based on completely residual nets.
Ms. Happy Kumari, Ms. Kajal Kumari, Ms. Preeti Gupta
Artificial Intelligence Driven Model for Gold Price Prediction
This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)–driven models. By applying the sophisticated machine learning methods, such as the Random Forest, Decision Tree, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error, the
study assesses the predictive power of these models by means of thorough evaluations. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions.
Keywords gold price prediction artificial intelligence, MSE.