Recreating Solar Panels: An Integrated Approach
As solar power becomes increasingly popular as a clean and renewable energy source, there is a need to better understand the technology used to convert sunlight into electricity. Bell Labs first used the photovoltaic effect to harness the striking of photons on a solar panel to create an electrical charge. In my paper, I propose two novel methods to increase the efficiency of solar panels. The first method utilizes solar tracking and computer science to tilt solar panels to face directly toward the sun throughout the day to maximize photon strikes. The second method concentrates photons through a magnifying glass to maximize the surface area of light being focused onto the panel. Developing better approaches such as those suggested in my paper have the potential to make solar panels more efficient and affordable, thereby increasing the scope of the solar power market.
Published by: Praneel Midha
Author: Praneel Midha
Paper ID: V7I5-1181
Paper Status: published
Published: September 13, 2021
A study of Machine Learning algorithms to predict liver cirrhosis and its stage
In the early stages, cirrhosis usually doesn’t cause symptoms. Only through routine blood tests or liver biopsy does a doctor diagnose damage to the liver. Using Machine learning we develop a model that can assist doctors in diagnosing the early stages of liver cirrhosis before it gets fatal. In this study, we use various machine learning algorithms to determine the liver cirrhosis stage. Data is collected from the Mayo Clinic trial, USA, in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The performance of the algorithms was evaluated using the ROC-AUC curve which is a very practical method of model evaluation for classification problems. Results showed that by applying Logistic Regression to predict the cirrhosis stage we get a ROC-AUC score of 0.74 which is considerable in view of the instance we have.
Published by: Prakash Aryan
Author: Prakash Aryan
Paper ID: V7I5-1200
Paper Status: published
Published: September 13, 2021
Facial expression recognition
Facial expression is a very difficult task (FER) due to the variation of facial expression across the human population and to the context-dependent variation even for the same individual. We can see that FER has many applications in the fields like human behavior interpretation and human-computer interface. The study of Mehrabian shows that 55% of the message is comprised of facial expression while 7% of the communication is conveyed by linguistic language whereas 38% by paralanguage during human communication. This shows that facial expression has a significant role in communication. The proposed system uses a webcam to capture our face and determine whether you were smiling or not. The system uses an OpenCV computer vision library to preprocess the webcam image, and in this system, we have used a logistic regression algorithm to train on a provided dataset and evaluate the new image.
Published by: Sakil Ansari, Rohan Raut
Author: Sakil Ansari
Paper ID: V7I5-1196
Paper Status: published
Published: September 13, 2021
Green Internet of Things in smart cities
‘Change is the only constant thing in the world.” The invention of the computer resulted in wanting more better computers and greater and advanced features. The same goes on for IoT. The Internet of Things also known as IoT is the ultimate building block for smart objects. A search for better and environmentally sound function resulted in Green IoT. A Smart city comprises all the basic necessities but in a smarter version. From the bed light before sleeping to the automated alarm to wake one up. Everyone is surrounded by Smart objects. These objects have definitely made our life easier. However, a change in life may have both good and bad consequences. Despite the benefits of IoT, energy consumption is still on the rise. This problem brought the solution motivated by achieving a sustainable smart city incorporated with Green IoT. This not only saves the extra energy which it was utilized priorly by IoT but also works efficiently. The Green IoT is a new advancement in technology and this paper discusses the merits, demerits, and various recommendations.
Published by: Sakshi Sushilkumar Patel
Author: Sakshi Sushilkumar Patel
Paper ID: V7I5-1195
Paper Status: published
Published: September 13, 2021
Perspectives regarding the quality of supervision among dental postgraduate students in Bengaluru city, Karnataka
Supervision is an important pillar in supporting the learning environment throughout post-graduate education. It is also one of the most important contributors to the successful completion of a higher education degree and to a student’s positive academic experience. Objectives:- The aim of the present study was to assess the perspectives regarding the quality of supervision among dental postgraduate students in Bengaluru city, Karnataka. Methods:- From a total of 18 dental colleges,9 colleges were randomly selected using a lottery method. All the postgraduate students from these selected dental colleges were included in the study. A total of 297 students participated in the study. The results were analyzed using SPSS, version 19(SPSS Inc. Chicago, IL, USA) in frequencies and percentages described as basic information. Results:- Overall, the majority of the post-graduate students were satisfied with the supervision they received and had similar perspectives about all the domains. Conclusions:- The study concluded that post-graduate students were satisfied with the overall supervision they received. The main elements contributing to a positive supervision experience were support, guidance, availability, and good communication between supervisees and supervisors.
Published by: Dr. Preetika Parmar, Dr. Radha G., Dr. Rekha R.
Author: Dr. Preetika Parmar
Paper ID: V7I5-1194
Paper Status: published
Published: September 13, 2021
Identifying Malicious Facebook Apps
Third-party apps are a big reason for Facebook's success and addictiveness, with 20 million installs every day [1]. Regrettably, hackers have discovered the potential of leveraging applications to propagate malware and spam. The issue is already serious, as we discovered that at least 13% of the apps in our database are malicious. Until far, researchers have concentrated their efforts on spotting malicious messages and campaigns. In this research, we examine whether we can tell if a Facebook application is malicious just by looking at it. The development of FRAppE—Rigorous Facebook's Application Evaluator—arguably the first tool focused on detecting fraudulent apps on Facebook— was a major contribution. We used data acquired by observing the posting behavior of 111K Facebook applications across 2.2 million Facebook users to create FRAppE. First, we identify a collection of characteristics that assist us in distinguishing between malicious and benign programs. As an example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging this FRAppE can detect malicious apps with 99.5 percent accuracy, no false positives, and a low false-negative rate, according to our findings (4.1 percent ). , We investigate the ecology of malicious Facebook apps and the ways by which they spread. Surprisingly, we discover that many apps collaborate and promote one another; in our dataset, 1,584 apps enable the viral spread of 3,723 additional apps through their posts. In the long run, we see FRAppE as a step toward establishing an independent watchdog for app evaluation and ranking, with the goal of alerting Facebook users before they install apps.
Published by: Dr. Ali Ahmed Razzaq
Author: Dr. Ali Ahmed Razzaq
Paper ID: V7I5-1192
Paper Status: published
Published: September 10, 2021
