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Indian Premier League (IPL) is a famous Twenty-20 League conducted by the Board of Control for Cricket in India (BCCI). It was started in April 2008 and completed its fifteen seasons in 2022. The current, i.e., the fifteenth IPL season, was held in May 2022. IPL is a popular sport where it has a large set of the audience throughout the country. Therefore, every cricket fan would be eager to know and predict the IPL match results. This project is about a detailed exploratory data analysis of IPL matches conducted from the year 2008 till matches held in 2019. Here, we analyze the overall IPL match scores, best batting and bowling performances, the team with a more significant number of wins, the most successful IPL team, the most valuable players and their best performance range, and so on. The complete dataset is collected from officials of BCCI of IPL matches held from 2008-2019 through Kaggle. Here, the testing accuracy of SVM classifier is highest at 0.9159, and the next highest is the Decision Tree algorithm which gave 0.8225 accuracies. The second highest is Logistic Regression, which gave 0.8159 accuracies, and the Random Forest algorithm, with 0.7563 accuracies. As the SVM classifier has the highest accuracy of all the four models, we use that model to develop the analyzer model for the project. [ABSTRACT FROM AUTHOR]

Digitalization of music is the new trend, and preferences of individuals are highly rated. Millions of songs are being streamed in the music applications. The companies providing these services need to sort and arrange a wide range of music tastes for all of its users. On top of that, fresh music from various artists in a wide spectrum of genres are popping up every day. To keep track of all this, a classification system can be handy. So, we propose an RNN based model based on Natural Language processing to classify the songs based on their lyrics into different genres [1]. Additionally, this tool can be handy to the music lovers for quickly identifying which genre a particular song belongs to. In this paper, we apply Long Short Term Memory (LSTM) model with both Universal Serial Embedder (USE) and Bert embedders. A comparative study is performed to understand which combination of models works based to classify the genres based on lyrics. From our results, on the basis of accuracy of the model, we found that USE embedder with LSTM [2] gives a slightly better performance than Bert embedder. The LSTM model with USE embedding gave the highest accuracy of 83.42% when trained over a range of five folds. [ABSTRACT FROM AUTHOR]

Chatbot is an intelligent system which simply defines human-to-machine interaction and this chatbots are biggest development trend today. Contacting customer centers or going to bank for banking related queries invests lot of time and human effort, further the customer may get insufficient information and may have uncertainty through this process. This paper aims to convey the development of Banking Chatbot that gives guidance regarding the services provided by bank and provides detailed descriptions to the user's query. This Chatbot is user friendly to communicate with and it is an easy way to get response on time. To overcome the difficulty web application using Natural language processing and neural network is developed [1]. NLP is an added advantage to make chatbot understand user queries. The operations in this Banking Chatbot include viewing beneficiaries and post queries regarding banking services. [ABSTRACT FROM AUTHOR]

This is an era of successful implementation of cryptography and Steganographic technique in cloud environment where people share their data to each other over the internet using different protocols like Http/Https, web sockets, FTP, SMTP etc. For the security aspects of data cryptography plays an important role. And due to increase in usages of the internet the cyber-attacks have also increased over the past few years. Thus, this problem gives rise to steganography techniques that is some advanced secret sharing techniques are evolved to hide data in other files after applying cryptography to the confidential/secret data. This paper speaks about the different existing cryptography and steganography techniques in cloud environments. [ABSTRACT FROM AUTHOR]

The recommendation systems become very important aspect in all areas like products ,movies , socials media and etc. Recommendation suggested from more overloaded information and analyze has to be done on massive amount of data. Apart from that media product like movie recommendation needs more process on previous and current data to suggest the user choice. In this paper we proposed a movie recommendation system based on the content and history based approach. This model starts the process by implementing of user similarity matrix. On the matrix results we applied the KNN to maps the user data to movie data based on the use choice. We also implemented the cosine similarity function between user choice to item data to find similarity and non similarity data outputs. This approach of movie recommendation system will do the process on history and content and produces the optimal results. [ABSTRACT FROM AUTHOR]

IoT based Fog Computing become major needy technology integration for innovative engineering. In the IoT based technologies the Fog nodes communicate with network edge node and creates easy interaction with Cloud data centers. The data which generated from the origin points transmit to destination in the network of different devices. In each device to process and execute the task needs some energy consumption. In this paper we proposed a Cost Function for Energy (CFE) which evaluate the total energy consumption during in transmission of data from source to destination based on the task. The total energy consumption depended on the total delay of task execution which presents amount of time for execution of a task. The CFE calculated for every tasks execution at local and other nodes of network. In our proposal we have included the greedy heuristic approach for evaluate the total delay accordingly the energy consumption. [ABSTRACT FROM AUTHOR]

The transport system plays a key role in the country's GDP, which leads to technological advances here. The smart transport system will increase the country's GDP and make it safer and secure. Implementing a smart transport system is possible by using IoT device services in the real world. Here it is going to simulate and implement the proposed method of vehicle-to-vehicle (V2V) communication. The experience of each vehicle is shared with the neighboring vehicle after establishing the ad-hoc network in the area. This leads to the Inter-Vehicle Communication (IVC) system. Each vehicle gains some experience from the history of data with the help of IoT sensors and keeps this information to sharing or for future use. This information may consist of road safety, driving conditions, weather conditions and vehicle conditions to prevent mishap or chaos. The vehicle needs can be addressed, such as fuel loss, engine condition and passenger emergency. This has been implemented on LabVIEW platform. [ABSTRACT FROM AUTHOR]

Economy of any country is investment in stock market. Stock price can be affected by factors which are running in current world and stock market. One of the most important things is to predict the how stock market will do. In stock market prediction machine learning has significant applications. Accurate stock market predication results are biggest challenge, because financial stock markets are volatile in nature. Forecasting stock market price is one of the most popular research areas for researchers. In this article, we had designed a model to predict the stock price of a particular company by analysing its previous data i.e. previous stock prices. We had analysed around 1300 stock prices of company, find the graph of company and created a model to predict present stock price. In this paper, we discussed about what is stock market prediction, how to predict with machine learning, analysed on some set of data and present results of our analysis. [ABSTRACT FROM AUTHOR]

Sarcasm is a mode of communication where people express themselves by writing it's contrary to what they really say. Sarcasm is a general phenomenon in social media. It is very difficult to identify and analyze in text. And also complex task because of challenges involved in determining the essence of the text containing sarcasm. The major difficulty in sarcasm detection is Ambiguity in nature. Sarcasm detection is very simple while face to face voice conversation based on pitch of tone, facial expressions, and gestures of opposite person. But in text conversation it is very difficult because sarcasm is conveying negative meaning with positive words. Sarcasm Detection difficult by human because if its challenges, so researchers have been working on this for the past decade. In this paper we discussed about approaches to detect sarcasm, types of sarcasm and issues involved in sarcasm detection. [ABSTRACT FROM AUTHOR]

Recommendation systems play a vital role in our day to day lives whether it may provide recommendation on which movie to watch or which item to buy. Most of the popular e-commerce websites are now a day's using recommender system gathering customer preferences like Amazon and Flipkart for selling their products. Similar to that we have music streaming applications like wynk and gaana.com which also provide recommendations to its users. These recommendations systems will benefit both the business and the users, the business are getting benefits and users are getting what they like.In this paper we have investigated music recommendation systems like content based filtering and collaborative filtering which provides music recommendations[1].We also tried to draw correlation between users and songs to provide recommendations as to which user would prefer to listen to which music based on their listening history. For this investigation we used the million song dataset available from kaggle.We used both the content based filtering and collaborate filtering algorithms to provide best music recommendation. Finally we also compared the accuracy levels of both the recommendation systems[2]. [ABSTRACT FROM AUTHOR]

One of the research emergence as per studied problem on the image processing based computer vision is that object detection in a image with bounding boxes. This complicated processing has to be done with help of machine leaning based algorithms only. In the recent years research has done with machine leaning algorithms like CNN,RCNN, Fast RCNN,FasterRCNN,Yolo algorithm and etc.These algorithms have achieved the proposed concept in different levels. In this paper we presented the comparative study of each algorithm and provided efficiency and weakness contexts. [ABSTRACT FROM AUTHOR]

Linguistic Correction is the process of showing the mistakes done by the users while framing a sentence. This includes grammatical errors, spelling errors, phonetic errors, and typing errors. Linguistic Correction helps the user to correct the mistake in their sentence. This allows the user to experiment and figure out the sentence mistakes. As mentioned above, the linguistic Correction helps the user figure out the above errors and answer whether the typed sentence by the user is grammatically correct or incorrect. The model trains the actual set of sentences and their results. One among them was the BERT model, where it reads or interprets the sentences from two ways, left and right, and does analysis, which gives us a trained model that can be put to figure out whether the sentences are grammatically incorrect or correct. [ABSTRACT FROM AUTHOR]

There is a tremendous increment of liver sickness patients as a result of exorbitant utilization of liquor, breathe in of hurtful gases, admission of polluted food, pickles and medications. Therefore lot of burden is put on the doctors to identify a patient whether he is having any liver disease are not.This paper helps to reduce the burden on the doctor by analyzing patients conditions using machine learning techniques[1]. We also make a comparison of few machine learning algorithms like random forest,logistic regression and SVM and compare their accuracy levels in predicting the liver disease. [ABSTRACT FROM AUTHOR]