An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens' life styles and hence, contribute to reducing the crime rates accordingly. © 2022 IEEE.

Haptic-enabled Virtual Laboratory for Hands-on E-Learning: A Technology for and beyond the Pandemic Era

Coronavirus disease 2019 (COVID-19) has spurred drastic changes in the tele-education worldwide, especially Pakistan. Starting from the first phase of pandemic, Pakistani students have witnessed extensive use of tele-school and other e-learning platforms However, e-learning platforms are majorly characterized by audiovisual capability and thus, student learning is limited due to the lack of "experimentation and interaction". To cater this problem, we propose a haptics and teleoperation based inclusive teaching, comprising of a few introductory concepts of physics namely, friction, viscosity, and inertia as pilot cases. The mechanism employs single-master (teacher), multiple-slaves (students) system, each having access to affordable haptic device 'Novint Falcon'. In teaching mode, apart from audio/visual, the demonstrator will explain concepts to students by interacting with force reflecting environments as master, whereas students feel the forces at their own haptic devices. Similarly, in other self-practice mode, students will independently perform experiments by haptic virtual interaction themselves. © 2021 IEEE.

Real-Time Patient Ailment Monitoring Framework Collaborating Enhanced CNN Architectures

As the deadly COVID-19 outbreak spreads across the globe, the utilization of IoT in the surveillance of patients can prevent us from facing catastrophic repercussions. This paper aims to develop a real-time health monitoring system where sensors are used to continuously observe the patient's body temperature, heart rate, and oxygen level. A comparison of two CNN architectures, VGG19 and DenseNet was also undertaken for audio signal processing, with VGG19 offering more promising accuracy in identifying coughing. Additionally, as severe coughing can be an alarm for lung diseases, the system identifies the number of consecutive coughing of a patient, as well as the timestamp for it. Moreover, if a patient feels infirm, they can seek assistance from a nearby doctor or nurse through Google's Speech-to-Text API. The data is then transmitted to a centralized database, where clinicians can monitor patients' symptoms in real-time by extracting the data via a web application. © 2021 IEEE.