Emerging smart fashion and its scope in the future
Smart Fashion has recently attracted the attention of the world and people are enthusiastically adopting it. It is a new form of fashion with technology embedded in it, which makes our everyday activities more efficient and less time-consuming. This study provides information about how smart fashion is developing and the different sectors where it is being actively adopted by professionals and governments. The paper discusses the emergence and continual growth of smart fashion in various domains: military, healthcare, sports and marine. Smart fashion for the elderly smart textiles and gadgets for the youth have also been included in the study. Smart Fashion has yet not been extensively adopted by the world yet, however, the research work shows the enormous potential it carries; it throws light on how with the advancement of technology, it will become more and more aesthetically pleasing and comfortable for commoners and industrials.
Published by: Gaurika Vaswani
Author: Gaurika Vaswani
Paper ID: V8I5-1148
Paper Status: published
Published: September 16, 2022
Carbon credits: Are they effective?
In today’s sustainable and environmentally responsible world, it is necessary to track and regulate a country’s CO2 emissions. This paper will evaluate if carbon credits in the form of carbon taxes have an effect on CO2 emissions and if there is, to what extent. The data used are from countries that have successfully established carbon tax. The paper will investigate the above by using a Linear Regression Model with the carbon tax, GDP per capita, and Area of the country as the explanatory variables and CO2 emission as a dependent variable.
Published by: Sanjana Saigal
Author: Sanjana Saigal
Paper ID: V8I5-1142
Paper Status: published
Published: September 16, 2022
Nine-layer CNN for detection of the cancerous growth of abnormal cells in the brain
A brain tumor is a group development of abnormalities brain cells. There are numerous forms of brain tumors. Some brain tumors are cancerous, whereas others are noncancerous. Brain tumor identification involves several phases, including the capture of an input MRI image, the conversion of the input image to a grayscale image, the application of filters, segmentation, feature extraction, and classification. The detection of a tumor is a difficult process. The position, size, and shape of the tumor differ greatly from patient to patient, making segmentation a difficult process. The detection of a tumour is a difficult process. The position, shape, and structure of the tumour vary significantly from patient to patient, making segmentation a difficult process. A Nine Layer CNN architecture including an input layer, zero padding, Conv2D, Batch Normalization, Re-Lu, Max pooling, Max pooling, Flatten, and Dense layer is designed in this study. TCIA Brain tumor dataset is used to train the Nine Layer CNN. TCIA dataset is augmented to overcome overfitting circumstances. In order to overcome overfitting conditions, the TCIA dataset is augmented. CNN nine layer produced a decent outcome, with a training accuracy of 98.93%. If the classifier determines that the picture is a tumor present image, it will also provide the proportion of the tumor.
Published by: J. Guna Keerthana, N. Britto Martin Paul, S. Sravan Kumar, B. Kavya Pranathi, V. Divya, Prudhvi Kanth Bezawada
Author: J. Guna Keerthana
Paper ID: V8I4-1196
Paper Status: published
Published: September 14, 2022
Credit Card Fraud Detection and Classification by Optimize Features and Deep Learning
Traditionally, rule-based systems have been the primary instrument for detecting fraud in today's financial systems, with fraud specialists defining the rules based on prior instances and outcomes. If a new transaction meets one or more of the previously established criteria, an alert is triggered, indicating that the new transaction may be fraudulent. For previously known fraud tendencies, the rule-based method is effective. Before adding a new rule to the current rule set, a sufficient number of fraudulent transactions must have happened that fit the rule. During this time span, fraud techniques may evolve, resulting in the induced rule expiring. Thus, the emphasis should be on using prior transactions that follow a rule-based approach in conjunction with an unsupervised method that detects previously unknown fraud activity. There is a need to use fraud detection systems that are capable of keeping up with the cardholder's updated spending behaviour. The detection process's goal is to identify as much fraud as possible while reducing the false positive rate, which has a negative effect on cardholder satisfaction as the cost of providing more false alarms increases. To accomplish this approach, the threshold value is determined at the account level of the cardholder by evaluating the probability sequence of previous and new incoming transactions. Additionally, identified fraudulent transactions are labelled in the database for future analysis in the event that additional assessment is required.
Published by: Razia Seema, Kumari Archana
Author: Razia Seema
Paper ID: V8I5-1146
Paper Status: published
Published: September 14, 2022
Intrusion Detection Classification and Detection by Machine Learning and Deep Learning Approaches: Review
DoS attacks that disrupt important, intelligent services like healthcare can also result in human death due to the disruption of routine services. devices (for example, intelligent refrigerators, smart televisions, and air conditioners) are easily attacked by attackers who exploit their weaknesses to launch denial-of-service attacks. As a result, one of the primary concerns for researchers all over the world is the protection of these devices. Globally, intrusion detection is being studied to fix this matter. IDS are classified into three categories based on their detection capabilities: Depending on a signature, a specification, or an anomaly. Whenever a device or network connections analyses an attack against a signature contained in the inner IDS database, an attack is identified by IDSs. If a device or network operation matches one of the saved signatures/patterns, a warning will be generated. This method is extremely reliable and effective at recognizing identified risks, and its process is simple to comprehend. However, this technique is ineffective in classifying new attacks and discrepancies between current These sorts of assaults do not have a meaningful signature to identify them If the divergence from a specified behaviour profile exceeds a predefined threshold, an anomaly-based intrusion detection system (IDS) issues an alert. Classifying intrusions does not seem to follow a typical pattern, and understanding the whole spectrum of normal activity is not an easy task. Emerging threats may be identified using this method. As a consequence, there is a significant rate of false positives with this method. Routing tables, protocols, and nodes, for example, are all part of the specification-based approach since they are all defined by a set of rules and criteria. It is possible to identify intrusions when network behaviour deviates from standards' specifications, using specification-based techniques. Therefore, specification-based detection is used for the same goal as anomaly detection: to separate aberrant behaviour from normal behaviour.
Published by: Ayush Gautam, Shivani Rana
Author: Ayush Gautam
Paper ID: V8I5-1147
Paper Status: published
Published: September 14, 2022
High school particle detector using Peltier cooling modules
This project aims to design a cloud chamber for use in a high school in order to educate and get students interested in particle physics. The cloud chamber shows the tracks of particles in real-time, which is excellent for engaging students. They will be able to identify different types of particles based on the appearance of their tracks, and will therefore gain a more intuitive understanding of particle physics. The inclusion of two compartments separated by a lead divider allows students to infer the direction of particle motion by observing its loss of energy. This cloud chamber also includes an interactive element, which comes from the introduction of a school-safe sample of radioactive metal and the manipulation of magnetic fields using an electromagnet. The cart is on wheels and is easily portable. It requires almost no time from the instructor outside of class, except for replacing the supply of isopropyl alcohol, which is inexpensive and easy to access.
Published by: Sadie Allspaw
Author: Sadie Allspaw
Paper ID: V8I4-1268
Paper Status: published
Published: September 12, 2022
