A Real-Time Monitoring System for the Elderly based on the GSM Network

Infectious illnesses are more prevalent and harmful in older adults because to functional, demographic, and immunological changes brought on by aging. Treatment for these disorders is complicated by aging organ systems. Given that many seniors have multiple illnesses and take multiple medications, antimicrobial therapy for them should be carefully chosen to prevent drug interactions and other issues. Thus, it is important to keep an eye on the health of elderly people. In this research, we tracked elderly patients using a clever technique. The device includes a microprocessor, temperature sensor, pulse sensor, LCD display, and GSM module. The temperature and pulse of the elderly are shown on an LCD monitor. The system may notify emergency services if the pulse and temperature data go over the permitted limitations. The method was successfully tested on a large number of elderly persons

Python TCP/IP libraries: A Review

The Internet's core is TCP/IP, which stands for Transmission Control Protocol/Internet Protocol. It connects network devices on the internet via communication protocols. Python has several TCP/IP packages due to its popularity and flexibility. This paper describes the most popular Python libraries for TCP/IP protocol implementation, including socket, asyncio, Twisted, and Scapy. To help developers choose a library, we compare its benefits, cons, and areas of use, including criteria other than speed and memory utilization. When making web apps, choose wisely

Modified Method of PAPR Reduction Using Clipping and Filtering for Image Transmission with OFDM

Due to the capability of OFDM (Orthogonal Frequency Division Multiplexing) to handle difficult channels, the most agreeable modulation for the multi-carrier scheme in present wireless communications is to improve an all-purpose modulation scheme, especially with high data rates. The image in this research article was transmitted and received on a noisy channel using an OFDM simulation technique. Since the average peak power ratio (PAPR) is one of the main disadvantages of OFDM, a new method has been proposed to reduce the PAPR using the clipping and filtering (CF) method. When the OFDM signal has a high PAPR, it means that many subcarrier components will be added through the operation of IFFT. Also, choosing the type of modulation to examine and getting a perfect type of OFDM system that is used for transmitting the image. Furthermore, signal-to-noise ratio (SNR) was considered to find the PAPR effect on the OFDM signal. The new method was tested to get a reduction of PAPR concerning CF and without CF. This method depends on clipping the signal before transmitting it, by using a method to overcome a nonlinear distortion, and therefore, decrease the bit error rate (BER). Then a filter with multi-stages was used to minimize the noise. This whole process was repeated several times to overcome the difficulties of transmitting/receiving the signal including PAPR. BER and SNR show wonderful outcomes when BPSK is chosen. Control over transmission and reception is also considered to be the type of modulation. All simulation results were defined using an Additive White Gaussian Noise (AWGN) channel

A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection

Network intrusion detection system (NIDS) has become a vital tool to protect information anddetect attacks in computer networks. The performance of NIDSs can be evaluated by the numberof detected attacks and false alarm rates. Machine learning (ML) methods are commonly usedfor developing intrusion detection systems and combating the rapid evolution in the pattern ofattacks. Although there are several methods proposed in the state-of-the-art, the development ofthe most effective method is still of research interest and needs to be developed. In this paper,we develop an optimized approach using an extreme gradient boosting (XGB) classifier withcorrelation-based feature selection for accurate intrusion detection systems. We adopt the XGBclassifier in the proposed approach because it can bring down both variance and bias and hasseveral advantages such as parallelization, regularization, sparsity awareness hardware optimization,and tree pruning. The XGB uses the max-depth parameter as a specified criterion toprune the trees and improve the performance significantly. The proposed approach selects thebest value of the max-depth parameter through an exhaustive search optimization algorithm.We evaluate the approach on the UNSW-NB15 dataset that imitates the modern-day attacks ofnetwork traffic. The experimental results show the ability of the proposed approach to classifyingthe type of attacks and normal traffic with high accuracy results compared with the currentstate-of-the-art work on the same dataset with the same partitioning ratio of the test set

A Serendipity-Oriented Personalized Trip Recommendation Model

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip