Efficiency Evaluation of HRF mechanism on EDoS attacks in Cloud Computing Services

The flightiness and unpredictability of the financial exchange render it trying to make a significant benefit utilizing any summed up conspire. This paper means to talk about our AI model, which can create a lot of gain in the US financial exchange by performing live exchanging in the Quantopian stage while utilizing assets liberated from cost. Our top methodology was to utilize outfit learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to choose whether to go long or short on a specific stock. As indicated by various examinations, stocks produce more noteworthy returns than different resources. Stock returns mostly come from capital increases and profits. Capital increases are the point at which you sell a specific stock at a more exorbitant cost than at which you bought it. Profits are a portion of the benefit that the organization whose stocks you bought makes, and disseminates it to its investors. As indicated by S&P Dow Jones Indices, beginning around 1926, profits have added to 33% of speculation returns while the other 66% have been contributed by capital increases. The possibility of purchasing shares from generally effective organizations like Apple, Amazon, Facebook, Google, and Netflix, together meant by the renowned abbreviation FAANG, during the beginning phases of stock exchanging can appear to be enticing. Financial backers with a high capacity to bear hazard would incline more towards capital additions for acquiring benefit rather than profits. Other people who lean toward a more safe methodology might decide to stay with stocks which have generally been known to give steady and huge profits.

A Review on Stock Prediction Using Machine Learning Algorithms

Meghwal

Khan

2022

Comparison of Segmentation-Based Image Compression Using Threshold, Region Growing, and Edge Detection

Chaturvedi

Khatri

2022

In the presenting paper we are dealing with to develop a lossless image compression (IC) method to utilize spatial redundancies inbuilt in image data which employs a best possible amount of segmentation information. To obtaining Multiscale segmentation we are using a earlier proposed transform which gives a tree-structured segmentation of the picture into regions which are identified by grayscale homogeneity. In the given proposed algor we have to shorten the tree to controlling the size and no of regions so that we can get a rate balance between the derived the coding gain and the operating cost inherent in coding the segmented data. Another uniqueness of the given proposed approach is that we are using an image model contain individual descriptions of the pixels lying close to the edges of a section and others lying in the center. In our results we can see that this proposed algorithm is providing better performance comparable to all the best available methods and it provides 15-20% better compression if we compare it with the JPEG lossless image compression standard for a enormous variety of images.

UGC Guidelines for Synchronization of Apprenticeship/ Internship in Degree Programmes of HEIs

Naveen¹

2022

With the sole objective of making the fresh graduates to be ready for employment with the necessary knowledge, competencies and the attitude, the UGC has framed the guidelines for higher educational institutions to offer apprenticeship and/or internship integrated degree programme at the under-graduate level. The scheme will focus on outcome-based learning and will equip the students to demonstrate workforce abilities for potential employment. The Apprenticeship/Internship degree programme will be embedded in the general stream with cooperation between industry and academia. This will bring a close relationship between education and industry/service sectors on a sustainable basis apart from helping the industry in securing good quality manpower. Across the global, Apprenticeship/Internship is considered as the most efficient and effective structured training programme for exposure to the real working environment. This strategy has an enormous scope to combine work-based learning with theoretical knowledge of respective disciplines. Amendments made in Apprenticeship Act/Rules during 2014-19 have given scope to link Apprenticeship programme to Education. Further, the UGC taking into consideration the present situation has framed new guidelines to improve the employability of students in the general stream ; to focus on outcome-based learning ; and to promote linkage between the degree programmes and industry. In this regard, the present article will introduce the readers with regard to the programme objectives, scope, duration, credit mechanism, assessment, learning outcome, role of HEIs, roles of Industry Associations, Sector Skill Councils and Board of Apprenticeship Training and lastly, monitoring by UGC. These guidelines have been designed keeping in view the visionary goal set by the NEP, 2020 for holistic development of the students.