Prediction and Anticipation Features-Based Intellectual Assistant in Location-Based Services

Abstract

Intellectual assistant in a location based service (LBS) is considered here as assistive technology in healthcare system for helping users with sensory, physical or intellectual diminished capabilities. The proposed system has the speculative computation module which helps in orientation for predicting the users’ error and alerting them for ensuring a true traveling direction well in advance for the handicapped people of mild to moderate condition of disability in earlier phases of cognitive disabilities. A person with cognitive impairments has the Trajectory (T) associated with them which is a sequenced set of past global positioning system (GPS) points. The implementation and evaluation of the trajectory data mining method have been done for the purpose of achieving the travel route to the user preferences. For the simulation of proposed system, a client server queuing model has been used. The results show the minimization of the overhead with better precision as compared to earlier existing path guiding systems.

Edge Computing Based on Federated Learning for Machine Monitoring

“…Each smart object in IoT is well organized, properly managed, and safely controlled for many intelligent applications in our daily lives [3]. Since IoT has been successfully applied in health care [4], agriculture [5], smart cities [6,7], and other applications [8,9], the development of IoT technology can also be used to help SMSF move toward Industry 4.0. Today, IoT refers more specifically to interconnected devices that combine sensors, software, and advanced technologies to transmit and receive data from the target.…”

Primary Mobile Image Analysis of Human Intestinal Worm Detection

“…In the field of parasitology, concepts of pattern recognitions are employed to diagnose medically relevant parasites (Daugschies, Imarom, & Bollwahn, 1999;Sommer, 1996). The concept takes three forms, thus, image pre-processing, feature extractions (Gupta & Shanker, 2021;Aggarwal, Mittal, & Bali, 2021), and classification (Lim, 1990;Jahne, 2005). Techniques adapting these fundamentals concepts in the diagnosis of intestinal worm depending on the characteristic of the dataset are artificial neural networks (Yang, Park, Kim, Choi, & Chai, 2001;Goundar, Prakash, Sadal, & Bhardwaj, 2020), adaptive network-based fuzzy inference system (Dogantekin, Yilmaza, Dogantekin, Avcic, & Sengurc, 2008), MultiClass Support Vector Machine classifier (Avci & Varol, 2009;Panda, 2019;Goundar, Sam;Bhardwaj, Akashdeep, 2021), active contours (Gupta, Bharadwaj, & Rastogi, 2021) and Bayesian classification system (Castañon, Fraga, Fernandez, Gruber, & Costa, 2007).…”