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Offer and Deal-Quality Prediction using machine learning and Fuzzy approach: A Shark Tank India Case Study 22

Offer and Deal-Quality Prediction using Machine learning and...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Atharva Parikh Shreya Jain Parikshit N. Mahalle Gitanjali Rahul Shinde Information Technology Vishwakarma Institute of Information Technology India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology India Computer Science and Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9781450399937

the Hindi-language television program Shark Tank India, which airs on Sony LIV, has changed the conversation in many Indian homes and raised awareness of entrepreneurship and start-ups. Shark Tank India depicts the background of each start-up and the business owners pitching their wholesome ideas to a panel of Sharks (Investors) who are entrepreneurs in various industries. Sharks then opt to invest in exchange for equity in the company (some percentage of ownership). Two of the major problems faced by startups before coming on the show is an understanding of what role each business factor plays in determining the proposition of offer by the sharks and the quality of deal done with or without negotiation with the sharks. To address this issue, we created a new dataset for Shark Tank India that contains essential features representing information about participating startups. then we used this data to propose two ambient systems. the first one uses machine learning to predict whether investors will make an offer to the pitching team, with the Artificial Neural Network (ANN) model performing best with an F1 score of 87.09%, and the second one uses a fuzzy system consisting of 22 rules to categorize the quality of a deal after an investment is made.

关键词： Artificial Neural Networks

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An Optimized Approach for Goods Based Delivery Network in Metropolitan Cities

An Optimized Approach for Goods Based Delivery Network in Me...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Avik Khanna Shivam Singh Panwar Avni Verma Preeti Dubey Department of Computer Science & Engineering Sharda University

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In the field of modern urban logistics, optimizing the delivery networks for goods in large, sprawling cities is a difficult but urgent task. the present study conducts a comprehensive analysis of this complex issue, exploring its diverse aspects and subtleties. the research weaves its way through the complexities of improving the efficacy and efficiency of these networks while emphasizing novel solutions. through careful investigation and consideration, the study benefits from the use of cutting edge technologies like ***, ***, and Flutter for solving issues like load balancing, real time tracking, and secure payment deals. the later sections of the paper present some empirical data and case studies which show how the suggestions make for practical gains in terms of transaction security, user experience, and system responsiveness. this comprehensive investigation reaches the conclusion that a successful delivery application must be an endeavor where it is necessary to join the frontend and backend technologies smoothly to deliver applications that are reliable, flexible and user centered, with zenith attention to urban requirements. Finally, the information provided in this study provides priceless methods and insights for streamlining goods based delivery networks, ensuring their continued applicability and efficiency in the dynamic surroundings of emerging urban logistics.

关键词： Technological innovation machine learning algorithms Scalability Urban areas Load management Real-time systems Routing protocols Security Optimization Logistics

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An Advanced machine learning Based Wear Optimization of Nano Particles Reinforced with High Strength Metal Matrix Composite

An Advanced Machine Learning Based Wear Optimization of Nano...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Narender Chinthamu T. Coumaressin Bechoo Lal Ismail Kakaravada Hrishikesh Haribhau Khodade B. Sundaramurthy MIT (Massachusetts Institute of Technology) C TO Candidate Enterprise Architect Wesco Boston MA USA Department of Mechanical Engineering Sri Manakula Vinayagar Engineering College Puducherry India Department of CSE Koneru Lakshmaiah Education Foundation(KLEF) - KL Deemed University Vaddeswaram Andhra Pradesh India Department of Mechanical Engineering Vignan's Institute of Information Technology Visakhapatnam India Department of Botany Rayat Shikshan Sanstha's S. M. Joshi College Hadapsar India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation Deemed University Salem Tamilnadu India

the use of Aluminium alloys in various industries has increased the demand for strong Aluminium alloys. To meet this demand, researchers are exploring new compositions for hybrid Aluminium metal matrix mixtures. In this study, an attempt was made to create a hybrid Aluminium alloy using stir-casting method and reinforcement with nanoparticles of boron nitride (BN) and nano zirconium dioxide (ZrO2) in 6070 Aluminium alloy. the Taguchi technique was used to enhance the stir-casting procedure limits that as agitation speed, agitation time, and temperature. the optimization was performed by varying the reinforcement content (0–12%) and the above-mentioned process parameters. the microhardness and wear resistance of the stir-cast materials were determined using a Vickers hardness tester and a wear tester. the results showed that the optimization of process parameters and reinforcement content had a significant impact on the microhardness and wear resistance of the Aluminium alloy. By analyzing the results, the optimum conditions for the stir-casting process were determined, leading to the production of Aluminium alloys with improved microhardness and reduced wear rate. In conclusion, this study demonstrated that the addition of BN and ZrO2 nanoparticles to 6070 Aluminium alloy using stir-casting method can enhance the microhardness and wear resistance of the alloy. the Taguchi method showed to be an effective tool in optimizing the stir-casting procedure limits, leading to the production of high-performance hybrid Aluminium alloys. machine learning technique is used to forecast the responses and the developed neural model predicts response at higher accuracy.

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application of machine learning algorithm for prediction of heart disease

Application of machine learning algorithm for prediction of ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankit Kumar Upadhyay Sharzeel Saleem Adarsh Anubhav Shrddha Sagar Department of Computer Science and Engineering Galgotias University Greater Noida India

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. this study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. the three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. this research will able to predict various disease over its various symptoms more easily and accurately.

关键词： Heart Technological innovation machine learning algorithms Jupiter Human intelligence Medical services Organizations

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Classification of brain ischemia and hemorrhagic stroke using a hybrid method

Classification of brain ischemia and hemorrhagic stroke usin...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Necip Çınar Buket Kaya Mehmet Kaya Dept. of Computer Engineering Dicle University Diyabakır Turkey Dept. of Electronics and Automation Fırat University Elazığ Turkey Dept. of Computer Engineering Fırat University Elazığ Turkey

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

the availability of artificial intelligence applications has expanded with the development of extremely powerful computers. this speeds up the diagnosis of many ailments and allows for the application of effective treatments. this facilitates the work of numerous experts in the field of health, and more trustworthy outcomes are attained. Many researchers have started working in this topic as a result of how swiftly artificial intelligence has entered human life and the promising outcomes that have been obtained. the detection of brain strokes from CT images, which are the most prevalent in the world and a major threat to human life, is a field that is currently seeing a flurry of deep learning and machine learning-based approaches. this study attempted to distinguish between ischemic and hemorrhagic brain stroke types using CT images. Utilizing a hybrid methodology, multi-classification was carried out utilizing EfficientNetB0 and SVM model from ischemic, hemorrhagic, and normal brain CT images in our dataset. the purpose of this study is to demonstrate a higher classification performance by combining the feature extraction skills of deep learning networks with the classification capability of machine learning models. the ischemic, hemorrhagic, and normal CT images in our dataset were used to train the EfficientNet-B0 + SVM, EfficientNet-B0 + KNN, ResNet50 + SVM, ResNet50 + KNN, ResNet50, VGG19 + SVM, VGG19 + KNN, and VGG19 models. there was a triple classification. According to the study, the hybrid EfficientNet-B0 + SVM architecture had the highest load success rate. the model had 95.13%, 94.93, 95.06, and 94.94% accuracy, recall, precision, and F1 values, respectively. these findings demonstrated that the EfficientNet-B0 + SVM architecture is a highly effective method for classifying different types of brain strokes from CT images.

关键词： Support vector machines Deep learning Accuracy Computed tomography Nearest neighbor methods Feature extraction Brain modeling

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Driver Drowsiness Detection Using computer Vision and machine learning Techniques

Driver Drowsiness Detection Using Computer Vision and Machin...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Ebenezer Essel Fatema A. Albalooshi Wael Elmedany Fred Lacy Yasser Ismail Department of Electrical Engineering Southern University and A&M College Baton Rouge LA U.S.A. College of Information Technology University of Bahrain Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

It is alarming that car crashes resulting in damage to automobiles, injuries to passengers, and loss of lives occur daily. the incidence of road crashes is rapidly increasing worldwide and has drawn many road safety commissions and concerned individuals to discuss ways to drastically reduce this menacing situation. the introduction of artificial intelligence and technological advancement has prompted government agencies and state commissions to call upon universities and research institutions to develop methods to curb the rise of automobile crashes. these crashes can be attributed to various factors, including drunk driving and, most notably, drowsiness, which affects a wide range of individuals. Drowsiness detection can be categorized into three main techniques; behavioral-based, vehicular-based, and physiological-based. In this research, the behavioral-based approach was studied, with significant consideration being the cost of implementation, execution time, and accuracy. three machine learning (ML) classifiers were considered: Support Vector machine (SVM), Naïve Bayes (NB), and Random Forest (RF). A dataset of 1448 images was used for training and testing these classifiers: 70% for training and 30% for testing. Random Forest classifier gave the best accuracy of (92.41) compared to SVM (90.34) and Naïve Bayes (69.43).

关键词： Support vector machines Training Accuracy Vectors Bayes methods Automobiles Random forests

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Inter-Vehicular Communication for Intelligent Collision Avoidance Using machine learning: An Overview 4th

Inter-Vehicular Communication for Intelligent Collision Avoi...

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4th international conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Pandya, Darshit Mer, Hiren Indus University Ahmedabad Gujarat India Department of Computer Engineering Indus University Ahmedabad Gujarat India

ISBN: (纸本)9789811636523

Following the inconsistencies encountered on the Indian terrain, the concept of autonomous vehicles has practically vanished from the Indian market. One major cause for the failure of self-driven vehicles in India is the collision occurring due to some common factors including poor lane-markings, deficiency of sign-boards, and most importantly – violation of traffic-rules by human drivers. Apart from the lack of markings, the lack of traffic-sense in human drivers is a major threat to autonomous driving in India;as no machine is capable enough to respond to the fluctuating mindset of a human being. In order to combat this challenge, a novel approach is presented that relies on the inter-vehicular communication strategy for avoiding critical collisions on the Indian roads. A unique application of machine learning technology to collision avoidance is outlined within this work. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Autonomous vehicles

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SAFE: A Secure Authenticated & Integrated Framework for E-learning 22

SAFE: A Secure Authenticated & Integrated Framework for E-le...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Monika Dandotiya Pankaj Rahi Ajay Khunteta A. Anushya Sayed Sayeed Ahmad CSE Poornima University India and CSE Poornima University India AI & DS Poornima Institute of Engineering and Technology India and AI & DS PIET Jaipur India FCE Poornima University India Faculty of Computer Science and Engineering Poornima University India Rochester Institute of Technology United Arab Emirates

ISBN: (纸本)9781450399937

E-learning is a relatively recent invention that focuses on learning over the internet as of this research. Every day, billions of individuals use web apps and e-learning platforms. E-learning facilitates the use of services at any hour of the day or night without restriction Web apps have become problematic in a secure and authenticated environment due to their widespread use. the initial goal of this study is to authenticate our e-learning system by increasing the length of the password and using Https instead of Http to implement a password pattern policy. For creating a website far more secure against hacking and other security flaws. the second goal is to enable AES in an e-learning system for safe encryption and cookie and session storage. the third goal is to mitigate attacks (XSS, man in the middle, phishing) using various HTTP headers, as well as apply security measures (functions and tokens) to prevent csrf attacks, which play different roles for different attacks and vulnerabilities, ensuring integrity principle and framing an authenticated and integrated E-learning framework (Moodle as a case). the proposed approach used password length increase with MD5+AES encryption and transmission over HTTPS protocol to achieve login authentication of a user on Moodle.

关键词： CSRF

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A Support Vector Regression based machine learning method for on-chip Aging Estimation 4

A Support Vector Regression based Machine Learning method fo...

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4th IEEE international conference on Computing and Information Sciences, ICCIS 2021

作者： Alnuayri, Turki Martinez, A. Leonel Hernandez Khursheed, Saqib Rossi, Daniele University of Liverpool Dep. of Electrical Engineering and Electronics Liverpool United Kingdom Dep. of Computer Engineering Taibah University Medina Saudi Arabia University of Pisa Dep. of Information Engineering Pisa Italy

ISBN: (纸本)9781665494410

Semiconductor supply chain industry is spread worldwide to reduce cost and to meet the electronic systems high demand for ICs, and with the era of internet of things (IoT), the estimated numbers of electronic devices will rise over trillions. this drift in the semiconductor supply chain produces high volume of e-waste, which affects integrated circuits (ICs) security and reliability through counterfeiting, i.e., recycled and remarked ICs. Utilising recycled IC as a new one or a remarked IC to upgrade its level into critical infrastructure such as defence or medical electronics may cause systems failure, compromising human lives and financial loss. this paper harvests aging degradation induced by BTI and HCI, observing frequency and discharge time affected by changes in drain current and sub-threshold leakage current over time, respectively. Such task is undertaken by Cadence simulations, implementing a 51-stage ring oscillator (51-RO) using 22nm CMOS technology library and aging model provided by GlobalFoundries (GF). machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, discharge time, frequency, and aging time. the data sampling is performed over an emulated 12 years period with four representative temperatures of 20° C, 40° C, 60° C, and 80° C with additional testing data from temperatures of 25° C and 50° C. the results demonstrate a high accuracy on aging estimation by SVR, reported as a normal distribution with the mean (μ) equal to 0.01 years (3.6 days) and a standard deviation (σ) of ±0.1 years (±36 days). © 2021 IEEE.

关键词： Integrated circuits

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Comparative Analysis of machine learning Algorithms for Prediction of PCOS 4

Comparative Analysis of Machine Learning Algorithms for Pred...

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4th international conference on Communication, Information and Computing Technology, ICCICT 2021

作者： Chauhan, Preeti Patil, Pooja Rane, Neha Raundale, Pooja Kanakia, Harshil MCA Department Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9781665404303

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting many women in their child-bearing age groups. It leads to sterility and various kinds of disorders in women. the main signs of this disease are uneven mensuration cycle, obesity, oily skin, pimples, anxiety disorders. An estimated one in five women suffers from PCOS. It is seen that most women overlook the common indication of PCOS and visit the doctor only when they face difficulty conceiving. If not diagnosed in time, the condition can cause serious health issues. To overcome this problem this paper proposes to develop an application for the early prediction of PCOS using machine learning techniques. the required dataset is created by carrying a survey and is cleaned using Python and Google Colab. Gini importance is used to compute feature importance. Classification of PCOS is done using various machine learning techniques such as K-Nearest Neighbor(KNN), Naive Bayes, Decision Tree Classifier, Support Vector machine(SVM), Logistic Regression(LR). Based on the accuracy and confusion matrix, the Decision Tree Classifier was found to be the most accurate model for PCOS prediction. A mobile application was developed which helps the user to predict PCOS at an early stage. ©2021 IEEE

关键词： Support vector machines

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