A Web Scraping of Chemical Compounds with an Anti-Drug Feature Using IoT

As a result of the COVID-19 epidemic, there has been an increase in the demand for electronic education apps in schools and colleges in recent years. The purpose of this paper is to develop an educational application for chemistry students at different levels of study that will allow them to obtain precise information on chemical substances in a timely and safe manner. This program uses a web scraping technique by applied a  RESRful API to extract information from websites, which is then sent to the student's account. Furthermore, due to the use of the Internet of Things, the application  has an anti-explosives and narcotics property using (IOT). The application can retrieve and save information entered by the user on chemical compounds with a high level of security. The medication's chemical formula, as well as the covalent and ionic bonding of compounds, can be displayed. It also has a database that lists all of the hazardous substances. If a user enters a dangerous compound containing narcotics more than four times, an alarm message is sent to the administrator via the Internet of Things

Review of Software Testing Methods

With the increasing complexity of programs comes an increased focus on ensuring the quality of these programs. Essentially, it depends on improving the methods of testing the quality of these programs in the two phases of building these programs and after their operation. Therefore, we are developing software testing methods and methodologies. This paper aims to discuss software testing methods and their classifications according to their main properties and any software that suits each method. The majority of the literature on software testing methods and techniques is also included

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

Landmark based shortest path detection in alarm system

In this paper, an alarm system for four types of emergency states (explosion, car accident, earthquake and fire) is developed. This system divided in to two parts: transmitted part (Arduino, sensors, GSM, GPS), and emergency part (center site, sub center sites). Center site is included (Android phone, Server), Sub center sites (helping centers) represented the mobile phones of competent authorities like police center or hospital. The alerting in this system as a SMS message is sent by GSM. The system used Haversine formula to determine the nearest sub center from emergency state that receives the SMS alarm message from transmitted part. Also the path is tracked using Google map application

Enhancing User Authentication with Facial Recognition and Feature-Based Credentials

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition

Secure Federated Learning with a Homomorphic Encryption Model

Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces "Secure Federated Learning with a Homomorphic Encryption Model," addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns

Automated Chemical Equation Balancing Using the Apriori Algorithm

Chemical equations must be balanced to maintain mass conservation. Traditional chemists employed manual processes with meticulous investigation and trial-and-error iterations. Automating and enhancing this difficult process is becoming more popular as machine learning (ML) progresses. We provide a novel Apriori algorithm-based chemical equation balancing method in this paper. Our solution uses the Apriori algorithm to find common itemsets of balanced reactions and translates unbalanced equations into machine-readable language. After that, it reconstructs balanced equations, automating a tedious task

Review of an Accurate System Utilizing GPS Technology

GPS provides precise position and control data anywhere on the planet and in any weather condition. GPS was originally intended for military usage, but in the 1980s, the US Department of Defense made it available for civilian use. The scientific applications of GPS in the military, community, and commercial sectors are expanding on a regular basis. Agriculture, construction, mining, measurement, package delivery, and logistical supply chain management all benefit from GPS technology. Precision GPS time synchronization is critical for big networks, navigation, finance systems, financial markets, and power grids. Wireless services are impossible to imagine without them. In this paper, we will go over the key aspects of GPS technology as well as a discussion of the systems that use it

A review of machine learning for big data analysis

Big data is the key to the success of many large technology companies right now. As more and more companies use it to store, analyze, and get value from their huge amounts of data, it gets harder for them to use the data they get in the best way. Most systems have come up with ways to use machine learning. In a real-time web system, data must be processed in a smart way at each node based on data that is spread out. As data privacy becomes a more important social issue, standardized learning has become a popular area of research to make it possible for different organizations to train machine learning models together while keeping privacy in mind. Researchers are becoming more interested in supporting more machine learning models that keep privacy in different ways. There is a need to build systems and infrastructure that make it easier for different standardized learning algorithms to be created. In this research, we look at and talk about the unified and distributed machine learning technology that is used to process large amounts of data. FedML is a Python program that let machine learning be used at any scale. It is a unified, distributed machine learning package