Computer – ASSET

Modeling and Visualizing the Impacts of Pets on Household Wellbeing

admin — Tue, 23 Dec 2025 19:19:07 +0000

Background: Pet ownership is universal. 66% of US households (86.9 million households) had a pet in 2024, and 97% of pet owners consider their pets to be a part of their family. Various studies show that caring for pets and interacting with them help pet owners improve their physical and mental wellbeing and, in turn, their life satisfaction.

Objective: Since pets become a vital part of the family, they should be considered in the economics of the household as other household members are. However, pets have barely been analyzed formally in economics or any other disciplines. Therefore, this work studies a formal model that applies economic principles, such as utility optimization, budget and time constraints, opportunity cost, and diminishing marginal utility, to describe and explain the household’s choices about having pets.

Methodology: Assuming the demand for pets is analogous to the demand for children, the proposed model follows the microeconomic theories of the household, particularly for fertility. Economic analysis of fertility was pioneered by Becker (1960, 1973), and his framework inspired numerous subsequent models (Doepke et al., 2023). The model in this work is particularly inspired by de la Croix and Doepke (2003) and Apps and Rees (2004).

The proposed model considers key economic parameters in pet ownership choices such as the household preference to have pets, the number and welfare of pets, the price of pet care goods and services, the time spent for caregiving, the household income, and the household utility (household wellbeing). Based on the model, this work formulates an optimization problem in which a household maximizes its utility as a rational economic agent by choosing an optimal combination of those economic parameters.

Results: This work solves the optimization problem analytically through a marginal cost-benefit analysis and finds the optimal number of pets and the optimal time spent for pet care with respect to the household utility. This work also carries out computational experiments by supplying empirical data to the model. Experimental results illustrate a utility landscape for average pet owners in the US and reveal the combinations of parameter values at and around the utility equilibrium.

This work is intended to aid the current and prospective pet owners to explore quantitative economic reasoning about pet ownership in the hope of reducing the chances of impulse pet acquisition, helping schedule pet care routines better, and reversing the on-going trend of pet relinquishment.

Becker, G. (1960) An economic analysis of fertility, Demographic and Economic Change in Developed Countries, Columbia University Press.

Becker, G. and Lewis, H. (1973) On the Interaction between the Quantity and Quality of Children, J Political Econ, 81(2):S279–S288.

Doepke, M., Hannusch, A., Kindermann, F. et al. (2023) The Economics of Fertility: A New Era, Handbook of the Economics of the Family, 1(1), Chapter 4, North-Holland, 2023.

de la Croix, D. and Doepke, M. (2003) Inequality and Growth: Why Differential Fertility Matters, Am Econ Rev, 93(4):1091–113.

Apps, P. and Rees, R. (2004) Fertility, Taxation and Family Policy, Scand J Econ, 106(4): 745–763.

Authors List :

Hanna Suzuki

Presenting Author :

Hanna Suzuki

Affiliations :

Bedford High School

Email :

Hanna.S.Suzuki@gmail.com

Key Words (5 Words Maximum) :

Household economics, computational economics, pet ownership, household wellbeing, economic optimization

AI in IT

admin — Fri, 06 Dec 2024 03:16:57 +0000

In recent years, the increase in prevalence of AI, especially in the tech industry, has caused many to wonder if it can cause problems for the IT industry. Like with most problems, it’s a mix of both good and bad. On one hand, as can be seen with Microsoft copilot and apple intelligence, it’s clear that AI can be used as a sort of stack overflow replacement, helping make code for specific examples, that, while still needing a specialist to correctly understand the code, makes said specialist’s job much easier. However, it does come with drawbacks. Most corporation need efficiency, meaning all this innovation does is most likely keeping the workload for the average specialist the same or higher, but in turn reducing the number of jobs. This makes IT a weird career that is both getting easier to do, while also having the amount of jobs decrease.

Authors List :

Ishan Palapra

Presenting Author :

Ishan Palapra

Email :

ishanishal2016@gmail.com

Crafting a Sound-LED Interface to Elevate Musical Visualization and Enrich Entertainment Experiences for Seniors.

admin — Fri, 15 Dec 2023 23:03:47 +0000

Background: During my volunteer experience playing jazz violin for senior individuals, I observed that many aging people gradually lose their hearing capability and eventually cease to enjoy listening to music. Additionally, in conversations with my grandparents, I discovered that they don’t derive as much enjoyment from music as they did in their younger years. Consequently, I envisioned a project to convert the tone of music into the ‘dancing pattern’ of LED lights. Through Python coding on Raspberry Pi connected to Arduino and LED, I aimed to create a device attached to the violin that senses the tone and volume of the music, converting the audio information into a dancing pattern of LED lights with varying colors.

Aims:
(1) I aim to test whether information such as tone (pitch), volume, and rhythm of the music can be extracted through the microphone connected to Raspberry Pi using Python coding.
(2) I aim to process the information and output the processed data through a digital-to-analog converting electrical circuit to feed into LEDs (through Arduino).

Novelty: While there have been many LED projects utilizing LED displays/strips as an interface for audio systems, many of these projects only extracted the intensity of the sound wave, simply feeding the information into the LED control. In my new project, I plan to extract not only the volume of each tone but also multiple parameters, including the tune of the music, wavelength, rhythm, etc. I intend to perform real-time signal processing to control the pattern and color of the LED strip, maximizing the impact of the visual effect.

Preliminary Progress: As illustrated in Figure 1, I have developed a Python code (Fig. 1) that enables LED strip illumination (Fig. 2) according to the sound picked up by a microphone.

Future Plans:
(1) We plan to upgrade the system to Raspberry Pi 4.
(2) We plan to interface Raspberry Pi 4 and Arduino in a master-servant relationship connected by serial communication.
(3) We plan to use a longer LED strip.
(4) We plan to implement a USB audio input interface to enable Raspberry Pi 4 to perform real-time signal processing.

Authors List :

Alyssa Yasuhara and Erica Yasuhara

Presenting Author :

Alyssa Yasuhara

Affiliations :

Day Middle School

Email :

alyasu929@gmail.com

Key Words (5 Words Maximum) :

LED music visualization, Raspberry PI, Arduino, Real-time signal processing, Audio-to LED conversion

Validating Wound Severity Assessment via Region-Anchored Convolutional Neural Network Model for Mobile Image-Based Size and Tissue Classification

admin — Wed, 13 Dec 2023 23:30:54 +0000

Evaluating and tracking the size of a wound is a crucial step in wound assessment. The measurement of various indicators on wounds over time plays a vital role in treating and managing crucial wounds. This article introduces the concept of utilizing mobile device-captured photographs to address this challenge. The research explores the application of digital technologies in the treatment of chronic wounds, offering tools to assist healthcare professionals in enhancing patient care and decision-making. Additionally, it investigates the use of deep learning (DL) algorithms along with the use of computer vision techniques to enhance the validation results of wounds. The proposed method involves tissue classification as well as visual recognition system. The wound’s region of interest (RoI) is determined using super pixel techniques, enabling the calculation of its wounded zone. A classification model based on the Region Anchored CNN framework is employed to detect and differentiate wounds and classify their tissues. The outcome demonstrates that the suggested method of DL, with visual methodologies to detect the shape of a wound and measure its size, achieves exceptional results. By utilizing Resnet50, an accuracy of 0.85 percent is obtained, while the Tissue Classification CNN exhibits a median deviation error of 2.91 and a precision range of 0.96%. These outcomes highlight the effectiveness of the methodology in real-world scenarios and its potential to enhance therapeutic treatments for patients with chronic wounds.

Authors List :

Sumaya Sanober

Presenting Author :

Sumaya Sanober

Affiliations :

Prince Sattam Bin Abdul Aziz University

Email :

sumayacsdept@gmail.com

Key Words (5 Words Maximum) :

wound care; deep learning; image validation; super pixel; mobile device; decision-making and region of interest

Cyber Security and Data Protection

admin — Sun, 10 Dec 2023 19:17:26 +0000

The internet has become more influential in daily life than anyone could have ever imagined. What was once a means of allowing US military computers to stay connected has transformed into an integral method of global communication.
But we don’t just use the internet to stay in touch with friends and family across the world. It’s also become key to how we operate businesses, store personal information and even manage our bank accounts. With so much important data being transferred across public networks every day, it’s no surprise that a small section of the technologically gifted chooses to exploit the more vulnerable among us. There are ways to protect yourself. Let’s assess how you might be at risk, whilst also explaining the steps you need to take to both prevent and recover from an attack. By its definition, cybercrime is the name given to any criminal activity that involves a computer or a networked device. Most often, a cybercriminal is someone who has a detailed knowledge of technology. They’ll often target networks which are unknowingly left unguarded but can also take active steps to try and infiltrate pre-existing firewalls. There are three primary types of cyberattacks: (i) Crimes where a computing device has been stolen. In these instances, data is illegally obtained from the device in question. (ii) Crimes where a computer is being used as a weapon. A hacker might be able to take control of a computer and use it to carry out further crimes. (iii) Crimes where a computer is used as an accessory. A criminal might even use someone else’s computer to store data they’ve obtained illegally as a way of keeping it separate from their own personal device. Ironically, it’s the accessibility and convenience that the internet provides which makes committing a cybercrime considerably easier. A criminal no longer needs to place themselves in the vicinity of the act. They can even use proxy servers and other cloaking devices to hide their identity, stealing your data from hundreds, maybe even thousands, of miles away.

Authors List :

Moidur Rahman

Presenting Author :

Moidur Rahman

Affiliations :

Jazan University, Saudi Arabia

Email :

moidur69@gmail.com

Key Words (5 Words Maximum) :

Cyber Security and data protection

Fusion Artificial Neural Network Models for Air Quality Prediction in Smart Cities Environment

admin — Tue, 13 Dec 2022 19:49:51 +0000

The growing concern for air pollution has been raised by many governments and people worldwide because it affects human health and sustainable development. Particularly, in India, the drastically deteriorating air quality threatens the health of its people. Meanwhile, in smart cities, knowledge of timely and reliable levels of air pollution is essential for the effective set-up of smart pollution systems. The current methods of air quality prediction primarily use shallow models which yield unsatisfactory results. Hence, it has inspired us to research methods of air quality prediction based on models of Neural Network (NN) architecture. In the present study, the NN architecture model was tested on air pollution data to improve the accuracy of prediction models. The two approaches proposed in this work are based on the Artificial Neural Network (ANN). The first method uses the Artificial Neural Network-Linear Vector Quantization (ANN-LVQ). It is an integration of the NN with LVQ, a significant Air Prediction Technique. The second method is a fusion of MultiLayer Perceptron Neural Network with Self Organization Map (MLPNN-SOM) technique. The main aims of implementing these methods are to provide early warnings by predicting air quality and estimate the influencing pollutant that contaminates the quality of air which thereby leads to air pollution. Upon analyzing literature review, feature extraction methods such as Feature Importance (FI), Principal Component Analysis (PCA), REFLIF-F and Self Organization Map (SOM) were considered based on the complexity of the problem. Feature selection is a process where the features in data are automatically selected to contribute to the prediction variable. After comparing the above-listed feature selection methods, the SOM method performed well in terms of accuracy and processing time. SOM visualization values were used as a similarity measure between the parameter that is to be forecasted and the parameters for the feature space. This method leads to the smallest set of parameters which surpass a similarity threshold. Next, to obtain the influencing pollutant that increases the AQI value, the second proposed fusion method (MLPNN-SOM) was employed to estimate the influencing pollutant which involves two phases. The first phase deals with the feature extraction technique. During the first phase, SOM was selected after comparing all other feature selection methods due to its accuracy and processing time. Based on the results, the proposed MLPNN-SOM method performed better than the other standard clustering. Network components such as activation function, learning rate, momentum, nodes and network structure strongly influence the classification performance of NN. Having said that, proper selection of weights and biases reduces the classification error. Therefore, in this study, the NN architecture was fine-tuned to enhance the classification performance by optimizing weights and biases deliberately.

Authors List :

Sumaya Sanober

Presenting Author :

Sumaya Sanober

Affiliations :

Department of Computer Science, Prince Sattam Bin Abdul Aziz University, Wadi Ad-Dawasir, KSA

Email :

s.sanober@psau.edu.sa

Technology as a Study Tool: Good or Bad – A high school student’s perspective

admin — Wed, 23 Nov 2022 02:44:06 +0000

The COVID-19 pandemic demonstrated to everyone that online or virtual classes could be vital for teaching and learning. The effective use of digital learning tools in classrooms could increase the students’ engagements, help teachers improve their lesson plans, and facilitate personalized learning. It also helps students build essential 21st-century skills. There are many advantages like access 24/7, multiple times of recalling lessons; However, there are many disadvantages like too much screen time, sedentary lifestyle, etc. Studies showed that more than 75% of academicians believe that the use of digital tools in the classroom make them more effective and efficient in their teaching and other activities. Because technology simplifies distributing assignments, accessing course materials, and eases much of the grading process and record-keeping. Further, it can also help educators make better use of their valuable time for things like lesson planning and assisting students in areas where they need extra help. From a cost perspective, it also reduces the cost of physical materials. Technology is a key which helps students to be more proactive and equipped for the next stages of both their education and their future career. Here, I will be explaining the positive and negative impact of technology in education.

Authors List :

Saeefh M Essa

Presenting Author :

Saeefh M Essa

Affiliations :

Northwest High School, Germantown, MD, USA

Email :

saeefhm@gmail.com

Key Words (5 Words Maximum) :

Technology, education, COVID-19, teachers, school educators

Why we have to study the brain?

admin — Fri, 21 Oct 2022 14:01:03 +0000

The brain is the ultimate computational machine in the universe. Despite what was thought about the brain decades and centuries ago, now it is well understood that the brain is a calculator, a machine that does computational things. In other words the brain is something that takes information in its input, elaborates it and then spit-out some result in form of
information or actions. If the brain is a computer, then it follows this conjecture: if we understand how our brain works, we can make computers that can think, be creative and work like a human brain (!!)

This challenging prospect is even more exiting because we know that nowadays, we DO know almost perfectly how neurons work (neurons are the only active element in the brain). Brains are neural networks. Nevertheless, we do NOT know now how the brain works, however if we could undertand that, it will be the most important discovery in human history.

Brain has been studied by life science people. Instead, Brain is a computational machine, it does computation. We need ALSO physicists, mathematicians and engineers to understand the Brain! In this talk I will explain why we have to study the brain, I will explain how recent scientist look at the brain as a computational machine, and give insight on the latest science related to brain functioning and artificial intelligence based on real brains.

Authors List :

Ruggero Micheletto

Presenting Author :

Ruggero Micheletto

Affiliations :

Yokohama City University, Cognitive Information Science laboratory

Email :

ruggero@yokohama-cu.ac.jp

Key Words (5 Words Maximum) :

Brain, Neuron, Artificial Intelligence, AI, Neural Networks

Deep Learning in Healthcare System for Quality of Service

admin — Tue, 27 Sep 2022 22:16:23 +0000

Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in curing diseases, diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that present novel works by ML and DL. Various targets are achieved through integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

Authors List :

Sumaya Sanober

Presenting Author :

Sumaya Sanober , Mohammed Abdul Mutalib, Mohammed Huda Aldosri

Affiliations :

Prince Sattam Bin Abdul Aziz University

Email :

s.sanober@pasu.edu.sa

Implementation of Cybersecurity in Public Domains

admin — Sun, 22 Aug 2021 21:17:23 +0000

Cybersecurity is the domain of networks, devices, and computer systems from unauthorized personnel to preserve the integrity of information. Cybersecurity is important because
it prevents data from being intercepted or altered. Data such as personal information and health information are especially sensitive and need to be protected from attempted theft. With the popularity of cloud services that store sensitive data on the rise, it is important as ever to maintain good cybersecurity. In this day and age, with almost every company out there having a website to promote their brand, cybercriminals have an easy time finding and taking advantage of poorly configured servers. Cybercrimes are dangerous because they can lead to severe economic repercussions. If private data were to be available for everyone, possession of intellectual property and trade secrets would be nonexistent. Additionally, companies with poor cybersecurity standards are in danger of leaking private customer information. Something like this would not only hurt the customers but the company itself, as they would lose consumer trust. As a result, it is vital to focus on cybersecurity. Companies can do this by educating themselves on the importance of cybersecurity and investing in tools to manage their cybersecurity. In order to truly strengthen the integrity of sensitive data, these are the measures that must be taken. This presentation will address the importance of implementing and maintaining good cybersecurity habits.

Presenting Author :

Abdul Khan

Email :

tanzil101614@gmail.com

Key Words (5 Words Maximum) :

Cybersecurity