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Result Management System Report for College Project The Result Management System is a crucial tool for educational institutions to efficiently manage and publish student results online. This project was developed as part of the 6th semester project for Pokhara University, with the aim of providing a user-friendly platform for students, faculty, and administrators to access and manage academic results. The system allows for easy input of student grades, calculation of final results, and generation of result reports. It also provides secure access to results for students through a login system, ensuring data privacy and confidentiality. To improve and increase the effectiveness of the Result Management System, several enhancements can be implemented. These include: 1. Integration of automated result processing algorithms to reduce manual data entry and calculation errors. 2. Implementation of a notification system to alert students and faculty when results are published or updated. 3. Addition of features such as result analysis tools, graphical representations of student performance, and trend analysis. 4. Enhancing the user interface to make it more intuitive and user-friendly for all stakeholders. 5. Incorporating a feedback mechanism to gather suggestions and improve the system based on user input. By incorporating these enhancements, the Result Management System can become a more robust and efficient tool for managing academic results online, benefiting both students and faculty alike.

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Data security is rapidly gaining importance as the volume of data companies collect, analyze and monetize grows exponentially. New data processing tools and platforms are emerging at an increasing rate, as are the ways in which an organization consumes data. In this presentation Mukund Sarma and Feni Chawla talk about the unique technical and cultural challenges of running a data security program and share some practical solutions that have worked well at our company. These slides were presented at the BSides Seattle 2024 conference.

BSides Seattle 2024 - Stopping Ethan Hunt From Taking Your Data.pptx

fenichawla

Java Programming :Event Handling(Types of Events)

simmis5

KubeKraft presentation @CloudNativeHooghly

sanyuktamishra911

Welcome to the April edition of WIPAC Monthly, the magazine brought to you by Water Industry Process Automation & Control. In this month's edition, along with the latest news from the industry we have articles on: The use of artificial intelligence and self-service platforms to improve water sustainability A feature article on measuring wastewater spills An article on the National Underground Asset Register Have a good month, Oliver

Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control

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Increased aeration of the soil; Stabilized soil structure; Higher and more diversified crop production; Better workability of the land; Earlier planting dates; Reduction of peak discharges by an increased temporary storage of water in the soil decomposition of organic matter; soil subsidence; reduced irrigation efficiency; increased risk of drought. excessive leaching of valuable nutrients from the soil; downstream environmental damage by salty or otherwise polluted drainage water; the presence of ditches, canals, and structures impending accessibility and interfering with other infrastructural elements of the land.

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mulugeta48

GAS POWER CYCLES Cycles: Otto, Diesel, Dual, Brayton - Calculation of mean effective pressure - Air standard efficiency - Comparison of cycles INTERNAL COMBUSTION ENGINES Classification - Components and their function - valve timing diagram and port timing diagram - actual and theoretical p-v diagram of two stroke and four stroke engines – carburettor - diesel pump and injector system - battery and magneto ignition system - principles of combustion and detonation in CI engines - lubrication and cooling systems - performance parameters and calculations.

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International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.

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Christo Ananth

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

Suman Jyoti

N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.

University management System project report..pdf

Kamal Acharya

Coefficient of Thermal Expansion and their Importance.pptx

Asutosh Ranjan

LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

Asst.prof M.Gokilavani

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