Babylonian Journal of Internet of Things

A Brief Review of Big Data in Healthcare: Challenges and Issues, Recent Developments, and Future Directions

2024-02-26T15:03:57+00:00

Big data is improving the healthcare industry and creating opportunities for improved patient care, personalized medicine, and advanced research. This brief review article aims to survey the challenges and issues connected with big data in healthcare, discuss recent developments in the field, and highlight future directions for helping big data to enhance healthcare outcomes by understanding these aspects, participants can make informed decisions and contribute to the advancement of big data analytics in healthcare.

An Author-Centric Framework Error Minimization in Scholarly Recommender System (Acfemsr)

2024-12-11T16:19:08+00:00

In the current landscape of computational innovations, the internet hosts a vast repository of publications sourced from various channels. Consequently, researchers encounter unprecedented challenges in identifying publications relevant to their research interests. Navigating through the multitude of options provided by search engines is not only impractical but also prone to selection errors – termed Recommendation Error (RE). These experiences underscore the need for novel research avenues. Previous studies have proposed numerous recommendation frameworks exclusively for scientific paper recommendations. However, many of these approaches have been plagued by RE, thereby compromising the integrity of recommendation systems. Recommendation Errors typically arise from underutilization of key features in the recommendation process. To mitigate Recommendation Errors, this study leverages publicly available metadata features, including Title, Abstract, Author(s), and keywords. Feature vectors for each candidate paper (CP) and the paper of interest (POI) are computed using CountVectorizer, and cosine similarity formula is employed to identify similar papers suitable for recommendations. The effectiveness of the proposed framework is evaluated using Error evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Finally, the proposed Framework is compared against two previous Baseline approaches. The evaluation results demonstrate that the proposed approach exhibits lower Recommendation Errors compared to its baseline counterparts. Additionally, this research highlights the Author(s) feature as the most influential among the four features utilized by the Proposed Framework.

A Real-Time Intrusion Detection System for DoS/DDoS Attack Classification in IoT Networks Using KNN-Neural Network Hybrid Technique

2024-07-10T14:13:49+00:00

As more devices are connected to the Internet through the Internet of Things (IoT), there are huge security challenges. One of the major problems is Distributed Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks floods networks with useless traffic, disrupting IoT services. There is a need for better security measures to handle this. Intrusion Detection Systems (IDS) is used to find suspicious activities, but many of them can't keep up with new types of attacks in real time. This study focuses on creating an efficient real time hybrid framework that uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks. This proposed framework aims to identify and categorize DoS/DDoS attacks in real time through the utilization of a simulation model and MQTT-IoT-IDS2020 dataset and compared with existing frameworks, our proposed framework excels in accuracy, precision, and recall.

Wheelchair Control Using Finger Gestures IoT-Based Arduino

2024-11-29T15:04:52+00:00

This paper presents the development of a wheelchair control system using Arduino and wireless finger gestures. The system incorporates various components such as Arduino Uno and Arduino Nano boards, ultrasonic and IR sensors, an LN298 motor driver module, motors, an IR flame/fire sensor, a buzzer, batteries, and HC12 wireless modules. The flex sensor is utilized to detect finger movements and gestures, while the ultrasonic and IR sensors provide obstacle detection capabilities. The Arduino boards process the sensor data and interpret the finger gestures, mapping them to specific commands for wheelchair control. The LN298 motor driver module interfaces with the motors to enable the execution of the desired motions based on the recognized gestures. IR flame/fire sensor monitors fire, and the buzzer provides audible feedback. The HC12 wireless modules facilitate wireless communication between the Arduino boards. The paper aims to create an intuitive and efficient control system for a wheelchair, allowing users to navigate their surroundings using wireless finger gestures while incorporating obstacle detection and air quality monitoring features.

System Development and Assessment For Road Vehicles Speed Detection Using GSM

2024-06-18T19:14:57+00:00

In metro cities, cars are not allowed to exceed the displayed speed limit. However, many drivers still choose to speed, which is a major contributing factor to accidents. To address this issue, an easy-to-use road speed check system has been implemented to automatically detect traffic crashes in smart cities. This system uses a photovoltaic (PV) input power supply source, which captures the light emitted by the sun on solar panels and converts it into energy. In addition, the system utilizes an IR sensor to measure the speed of vehicles by detecting their distance and time. The infrared sensor detects a car in front of it, allowing for the calculation of time. A proximity sensor is also used to detect the speed of vehicles, regardless of their material configuration. The ESP8266 interface camera is used to capture live images in traffic, which are then transmitted to a specific IP address. Furthermore, the GSM Communication Device allows for the transmission of vehicle speed information through digital cellular communications, such as GSM (Global System for Mobile Communications). The output of this system accurately reflects real-world traffic data and has been tested through a comparison examination of individual speed measurements

Monitoring Pollution in Smart Cities Based on Arduino and IoT

2024-10-28T20:05:28+00:00

This study offerings an innovative solution for checking pollution in smart cities using the Internet of Things (IoT) and Arduino-based sensors. As urban areas face increasing air pollution levels, the need for effective monitoring systems becomes necessary. This paper focuses on creating a scalable, secure IoT architecture to collect, analyze, and manage air quality data in real-time. The system utilizes MQTT for secure data transmission, and machine learning algorithms for predictive analysis. A model is developed using Arduino sensors to measure carbon dioxide, butane gas, temperature, and humidity. The outcomes indicate the system’s ability to effectively monitor pollution, notify users through mobile applications, and provide actionable insights for pollution management. Future work will improve the system’s abilities, including expanded scalability and additional environmental parameters.

Real time Cloud based fishes health monitoring using IoT.

2024-04-17T09:20:50+00:00

This work is based on a prototype model for the Fishery Management System with the help of Internet of Things i.e., IOT.

The proposed system works on IOT & provides real-time monitoring of the fishery system. With the help of various hardware & software devices like Sensors & the Arduino Cloud, data from the fishery system can be collected and analysed using the Cloud which also helps in maintenance of the fishes from time to time. This also helps in creating awareness about the fish whenever necessary.

Along with IOT, Water Level Sensor, Temperature Sensor & pH Sensor is also used to measure the water level, temperature level and get the pH values in the system. The system can be made automatic as well as configured according to the requirement for the survival of the fishes. The Node MCU ESP8266 helps in collecting the information through the sensors & display it in the Cloud which can be accessed from Smartphones, PCs, and Tablets etc.

Evaluating the Effectiveness of Machine Learning-Based Intrusion Detection in Multi-Cloud Environments

2024-09-15T19:53:46+00:00

In the dynamic landscape of cloud computing, multi-cloud environments have emerged as the prevalent ones due to offering a host of advantages including redundancy, flexibility, and increased security. These multi-cloud environments can, however, be vulnerable to stealthy cyber-attacks, thereby making it a challenging job to find efficient intrusion detection systems (IDS).

The efficiency of the Machine Learning-based Intrusion Detection System in multi-cloud environments has been evaluated using a 1-D Convolutional Neural Network to classify network traffic based on the UNSW-NB15 dataset. The dataset included a wide variety of network features, and it was decided that it could be used directly after converting the categorical variables into their numerical forms and scaling them with MinMaxScaler.

This was used for training and testing the 1D-CNN model, which had attained an accuracy of 84.02% during training and 82.79% on validation. Further, the performance of the model is elicited with metrics that include precision at 80.91%, recall at 82.79%, and an F1-score at 81.28%, showing its capability in identifying network intrusions effectively.

The small difference between the accuracy of training and validation shows that overfitting was minimal; thus, the model will generalize well when applied to unseen data. This research underlines the possibility of using deep learning techniques, and particularly CNNs, for intrusion detection in complex multi-cloud infrastructures. Probably future work would be optimizing the model through hyperparameter tuning, using additional sources of data, and even exploring much more sophisticated architectures that will further improve the accuracy of intrusion detection.

Impact of IoT-Based Environmental Monitoring on Lab Safety and Sustainability

2024-03-01T03:38:10+00:00

Several internal and external dangers might threaten a laboratory, the institution, and the public if not addressed properly. These hazards are addressed by our extensive laboratory monitoring system. This device detects elevated temperatures, gas leaks, fires, and solution pH. Elements of this system include an Esp32 microcontroller, LCD, Arduino Uno, mq2 gas sensor, DS18B20 temperature sensor, flame sensor, relay, water pump, and pH sensor for acidity measurement. The Arduino IDE controls sensors. Once the temperature reaches 50 degrees Celsius, an alarm, red LED, and mobile phone notification activate. Gas leaks activate a green LED, an alarm, and a message. Flame sensors notify and start water pumps when fires occur. Gas leaks and fires activate a buzzer. The IoT offers essential incident reporting and remote laboratory monitoring. The code is written in Arduino C++.

Analyzing and Comparing Global Sustainability Standards: LEED, BREEAM, and PBRS in Green Building arch article topic

2024-07-10T14:44:36+00:00

Since the year 2000, a heightened environmental awareness has led to the emergence of global trends in forefront countries, prompting the need for standardized practices in the environmental building industry. This paper explores various experiments on buildings, demonstrating trends through examinations and tests conducted by governmental or private organizations. Notable among these trends are the standards set by global evaluation systems such as BREEAM, LEED, PBRS, and SSBS. The paper highlights the recent adoptaion of Arab standards in Abu Dhabi, positioning the emirate as a pioneer in sustainable development. The research focuses on the city of Kirkuk, where building regulations lack consideration for environmental standards. The study aims to identify applicable international standards, emphasizing the importance of residential standards in comparison to other criteria. The World Green Building Council's universal standards for existing buildings are discussed, with the recognition that these standards may need customization to align with the local environment and architectural characteristics of Kirkuk. The paper delves into the methodology used, employing a theoretical framework, analytical methodology, and a deductive approach to formulate recommendations tailored to meet local standards. The Abu Dhabi experience with the Pearl Rating System is explored, outlining its three evaluation stages and the specific criteria for building assessment. A comparison between sustainability standards reveals disparities in evaluation criteria, emphasizing the challenge in establishing global standards. The study calls for a transparent and globally applicable evaluation basis, considering discrepancies among diverse countries and their respective tools. The paper concludes by underlining the importance of establishing local standards, serving as a benchmark for quality and operational efficiency within the real estate market. It also emphasizes the crucial role of existing residential buildings in proposed solutions for sustainable development, highlighting their significance in local markets.

System design and implementation of an IoT electronic pulse sphygmomanometer

2024-01-01T01:52:34+00:00

A sphygmomanometer is a standard testing tool for monitoring blood pressure and plays a vital role in modern medical diagnosis. Traditional smart electronic sphygmomanometers are complex and costly; this paper designs a low-cost electronic pulse sphygmomanometer through microcontroller control. This design collects the gas pressure information through the barometric pressure sensor and inputs it into the analog-to-digital converter. It converts the collected analog signal into a digital signal and then completes the determination of blood pressure and pulse through the recognition algorithm. Finally, this design is found to have the advantages oa f wide range of applicationslightweightht, low price, etc., and has important application value.

Construction a virtual central network for all Iraq's fire station

2024-12-02T17:10:49+00:00

Fire stations are crucial in Iraq for protecting human life, property, and vital economic and religious infrastructure. In this paper, a design for a centralized virtual network that brings together all fire stations in Iraq is proposed. The network consists of 18 stations, with each station representing a governorate in Iraq. When a fire occurs in a certain area, citizens can request the service by entering their personal information and the location of the event, which is sent to the centralized network that transfers it to the appropriate station in the area. If the station is not available in the area, the request is sent to the nearest other station to fulfil the request as quickly as possible. This virtual communication network allows for better coordination and simplifies data sharing and communication between different stations. The results showed that the centralized network design contributes to fast response times, rapid response to requests and rapid deployment of fire engines to the incident site. Fire stations can also increase overall efficiency by improving coordination, allocating resources more effectively and monitoring situations in real time. Thanks to the centralization of data, all stations have access to the same set of information, enhancing decision-making, especially in the case of large-scale incidents. Also, the network design is characterized by a high level of security.

Intelligent Watering by Using Solar Energy System

2024-07-06T02:48:00+00:00

In this study, Kirkuk and its surrounding areas are evaluated to determine whether solar energy can be utilized for irrigation. PV panels create their own power (300 W). A meter is used to measure the depth of a well (h) (60, 70, and 80 m). Graphics demonstrating the link between solar radiation, angle of solar radiation, and sun brightness in each month were generated using data from Iraq's Meteorological Organization and Seismology. Graphs depicted the volume flow rate at each depth. A study found that using PV panels to generate power increased volume flow rate, in addition to being more efficient throughout the day and in the summer (summer season). The depth of the well and the rate of flow are inversely proportional to one another. Because the flow rate of the system is suitable for most crops grown in the agricultural area of the city, solar energy generated by a photovoltaic (PV) panel may be used for irrigation in Kirkuk.

IoT of Healthcare Innovation Solutions for Predictable Virus Detection

2024-11-25T03:41:00+00:00

The evolution of health care systems from old models to more personalized frameworks facilitates easier diagnosis, treatment, and monitoring of patients, benefiting numerous individuals. Individuals receive remote treatment and care, which is essential for some during the global crisis exemplified by Predictable Virus Detection. This manuscript, A medical platform features an architecture reliant on middleware and database support for individuals with Coronavirus, primarily serving three user categories. The administrator is categorized into two user groups: doctors and patients. The doctor has an app that questions through the patient so he knows the patient that is being visited and extracts the health identity from him, and he questions the patient for sending him an OTP in the event that the patient does not have a mobile screen or an Internet connection. Alternatively, if QR inquires whether his laptop is intelligent and connected to the Internet. The individual will gain access to the system subsequent to the doctor's examination. The patient will conduct a self-examination using his equipment and present the results to his doctor. The physician is able to provide a prescription each time he transmits new readings. If the prescription is accurate, retain it and escalate the dosage; do not incorporate an additional dose. Physicians will utilize the prescription interface to transmit the prescription for cloud authentication and acquire an encrypted QR code, which will subsequently be provided to the medication receiver. The patient is afforded the opportunity to examine medicine information through the recipient's application. The entitlement to access QR protected cloud data is perpetual. The pharmacy can dispense the medication just as prescribed until the QR code's expiration date. The initiative aims to enhance access to healthcare facilities for patients and physicians, while adhering to GDPR regulations.

Empowering Aquarists a Comprehensive Study On IOT-Enabled Smart Aquarium Systems For Remote Monitoring And Control

2024-06-14T14:18:45+00:00

owadays, a lot of people keep fish as pets at home and the aquarium keeper has been feeding the fish in the tanks, therefore appropriate setup and upkeep are required. Taking care of the aquarium appears to be somewhat tough for aquarists. The fish's quality of life depends on the aquarium being kept up properly. Changes in water quality, fish feeding, temperature management, light control, and the challenge of physically inspecting aquarium conditions are these problems are experienced.. As such, it is important to monitor the physical characteristics regularly and to improve the quality of the water. It has water quality management, which levels the aquarium to the perfect conditions by keeping an eye on the physical variations in the tank. Thus, the system that this model suggests is capable of real-time operation and is fitted with sensors. It carries out turbidity system, water renewal, temperature monitoring, and pH level detection in water and all these things are carried out in the ESP32 controller which contains an inbuit WiFi module. For large-scale aquaculture, an embedded system with a wireless network technology and a water quality testing unit was proposed. The aquarium's state is tracked and sent to the user's mobile application via Thingspeak IoT platfrom.

RNN-Based Framework for IoT Healthcare Security for Improving Anomaly Detection and System Integrity

2024-10-13T11:15:10+00:00

The rapid rise of the Internet of Things greatly benefited the healthcare sector by opening up new ways of monitoring patients and ingeniously collecting data on disease management. However, this increased connectivity and health system data interchange introduce many critical security vulnerabilities that are more likely to discredit highly sensitive patient information along with system integrity. The paper investigates only one critical IoT health care security issue, for which a new security framework based on RNNs was used to investigate enhancements in the threat detection and response. This approach modelled network traffic and device behavior sequentially for anomaly and potential breach detection using RNNs. Hence, we introduce the RNN-based model combined with an inclusive security architecture, including data encryption, mechanisms of authentication, and monitoring tools in real time. Experimental results prove that our RNN-based framework significantly improves malicious activity detection and reduces false positives compared to traditional security solutions. The proposed model would provide a strong, scalable, and adaptable security solution tailored to the IoT healthcare environment dynamics. These findings could indicate how RNNs can enhance security in IoTs and provide new ways in which better and more secure healthcare systems can be developed.

Role of Food and Drugs Authority Act, 1992 (PNDCL 305B) and Legislative Instrument (LI) in Regulating Artificial Intelligence Based Medical Devices, Apps, and Systems to Prevent Negligence

2024-03-24T17:28:49+00:00

Objective: This analysis evaluates whether Ghana’s Food and Drugs Authority Act provides adequate statutory basis and policy tools for the medical regulatory authority to evaluate risks and oversee artificial intelligence (AI) based software devices, given complexities of AI technology.

Methods: Case examples of medical AI systems and international regulatory issues establish current challenges for regulators. Specific sections of the Act are analysed regarding definitions, requirements for safety and efficacy evidence, guidelines for changes to approved devices, and post-market powers.

Results and Conclusions: Current law does not provide explicit provisions to require transparency into AI systems’ decision-making or continuous monitoring of real-world performance. However added clauses on privacy and cybersecurity demonstrate a policy precedent for regulating complex technologies.

Recommendations: Updates should enable differentiated evaluation approach based on risk-tiers. Powers for inspecting algorithmic logic require expansion to address AI opacity.

Novelty and Significance: This represents the first analysis situating medical AI governance within Ghanaian statutes. Stress-testing decades old policy structures against emerging technology risks is both scientifically novel and significant for public health oversight.

CNN- based intrusion detection software for network operating system environment

2024-08-17T12:13:16+00:00

Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzing the natural behavior of the system by building a special convolutional network to achieve this goal. The classification and detection results of the proposed convolutional neural network were compared with the regular machine learning method (SVM), with feature selection by correlation for both methods. Same datasets were used to train and test each of CNN and SVM. Some metrics were determined to evaluate the performance of classification and prediction models for a specific type of regular attacks, DoS and BOT attacks, where both SVM and CNN obtained an accuracy of 85.58% and 95.59%, respectively.