Intelligent Appliances Research Articles

A Web-Based Academic Article Recommendation System: Survey

The rise of the internet and intelligent gadgets has increased platform traffic, collecting data to detect user preferences. Researchers need help finding relevant material due to the rapid proliferation of academic papers across fields. Academic article recommendation systems (ARSs) help solve this problem by suggesting articles based on research interests and needs. Academic article recommendation algorithms help navigate the vast scholarly literature. Researchers, students, and academics utilize them to find articles that match their interests and study emphasis in enormous databases. This review covers recommendation algorithms, data sources, assessment metrics, and user interfaces in ARS research. This survey also examines new trends and research directions, such as advanced machine learning and semantic analysis

An IoT system based on open APIs and geolocation for the prevention of human health disorders

The article presents a study devoted to improving the developed Internet of Things system based on open APIs and geolocation, which is aimed at analyzing data about the state of the environment using an expert approach and data visualization for possible prevention of human health disorders. Based on the developed Internet of Things system, open APIs, geolocation using intelligent gadgets, and the Meteorological Geographic Information System, the study generates a message about the danger to human health associated with meteorological factors. Accordingly, a person is informed promptly about potential risks and threats, particularly about the presence of pollen in the air, indicating the level of its concentration in the air, and about problems with air quality. What is the “anthropo-geo-sensory-digital” prerequisite for making effective real-time decisions to prevent human health disorders? New features were added to the developed system to analyze data about potential risks and threats that could lead to human health disorders, in particular, about the presence of temperature problems, under the condition that this indicator goes beyond the normative and optimal zone; the presence of relative humidity problems, under the condition that this indicator go beyond the normative and optimal zone; the presence of wind speed problems, if the air wind speed exceeds the permissible standards. Effective decision-making based on providing timely information about potential risks and threats to human health, in addition to preventive, has significant methodological and technological potential that can be used to improve the effectiveness of health care, both in extreme conditions and in conditions of sustainable existence. The system developed and improved by us can also be considered as one of the ways of introducing innovations in health care, the IT field, the educational process in institutions of higher education and conducting further research in this field, in particular, in the direction of data processing in health care systems based on machine learning.

AI Kitchen

Purpose: Using several smart gadgets makes the kitchen smart. The picture of the kitchen from ancient has drastically changed. Now, the kitchen is glorified using modern technology. The kitchen chimney, microwave oven, etc., is more convenient for the kitchen. Now, AI has come, and every day, more and more devices are becoming AI-enabled. In this scenario, we demonstrated the project in an AI-enabled kitchen. There are several advantages of AI-enabled kitchens over intelligent kitchens. AI will handle our most repetitive and monotonous work. Also, AI will take care and protect from any accident before it happens, which generally happens due to forgetfulness or carelessness. Design/Methodology/Approach: We installed a CPU inside the home. We installed an action controller in the kitchen that connects all kitchen gadgets. One PTZ camera is installed in such a place that captures the gas oven and the entire kitchen environment. Our camera is the point-to-zoom (PTZ) type, so it can rotate the lens and zoom to capture any incident for better understanding or to detect the perfect image for accurate detection. Our camera is running all the time, which means round the clock. It always captures the image and processes the image by CPU. If any event matches with the event database, it sends the command to the action controller to take the action. If a new event is detected, it will learn from it and save it into the database for future use. Findings/Result: The complete system is the conceptual-based research work. However, every part of the module or section is based on practical research work. So, once the system is deployed in the practical field, it will work without issues. In a typical kitchen, we must always pay attention, like the regulator is on if the milk is left for a long time in the gas oven. So, this kind of tension and anxiety will go for retired. All events will be taken care of by our AI-enabled system. Once the system is installed, it will run autonomously; there is no need to take care of or follow up. Once the system detects some issue, the system will notify the concerned person. Originality/Value/Novelty: Nowadays, intelligent gadgets are taking place in the home. Smart devices, especially in the kitchen, make life easy. Using AI, we can get more benefits and security for the kitchen. We studied several research works. Most of the researchers automated kitchen gadgets. But still, the problem exists. All gadgets should be under centralized control to get better safety and control. Through this research work, we described how to create safety and control in the kitchen environment. This research works to provide more value to the modern, smart kitchen. Type of Paper: Conceptual Research.

Revolutionizing collaborative auditing: A dynamic blockchain-based cloud storage framework for data updates and assurance

Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services.

ThumbUp: Secure Smartwatch Controller for Smart Homes Using Simple Hand Gestures

The development of creative applications and intelligent gadgets requires a secure and straightforward interface with human users. We propose, design, and implement ThumbUp, a smartwatch-based two-factor real-time identification and authentication system in which smartwatch users can identify and authenticate themselves using some simple hand and finger movements, such as thumb-up. ThumbUp leverages the signal from the Inertial Measurement Unit (IMU) in in Commercial-Off-The-Shelf (COTS) smart devices to discover the unique pattern generated by each user's simple gestures using a carefully constructed deep learning model. Smart homes provide a comfortable, safe, and efficient living environment, epecially help the sick and aged. We propose strategies for convenient and reliable control in smart homes with gesture command recognition. We build an Auto-Encoder-based filter that reconstructs the raw data to improve the representation of gesture features. Moreover, we adopt the random forest method to analyse the contextual command correlation. And we employ the authentication system based on smartwatch for personalized command feedback and ensure that illegals cannot use the device. We implement our system and undertake rigorous studies to determine its usefulness and efficiency over a three-month period with 65 users. It achieves a 97% accuracy for user classification and an EER of 0.014 for authentication task with a single simple gesture. And our method achieves 91% accuracy for command recognition and 96% command accuracy with contextual informations. Additionally, we conduct a study of user acceptability of our system and explain how gesture proficiency influences authentication accuracy.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Artificial Intelligence and Machine Learning for Real-time Energy Demand Response and Load Management

Within this compendium, an exhaustive examination is undertaken to scrutinize the intricate amalgamation of artificial intelligence (AI) and machine learning (ML) techniques within the paradigm of real-time energy demand response and load management. Placing paramount importance on the pervasive significance of AI and ML, this research expounds upon their profound capabilities to adroitly harmonize the delicate interplay between supply and demand, meticulously calibrate the multifarious dimensions of grid stability, and optimize the boundless potential inherent in renewable energy resources. An in-depth analysis ensues, encompassing the deployment of AI algorithms, poised at the vanguard of demand response optimization, and the judicious utilization of ML techniques, flawlessly calibrated to deliver unerring accuracy across varying temporal scales in the realm of load forecasting. Furthermore, the seamless integration of AI into the very fabric of intelligent appliances and Internet of Things (IoT)-enabled systems unfolds, illuminating the path towards energy consumption optimization, ascertaining an intricate tapestry of interconnected devices, and engendering an ecosystem of intelligent load management. Notably, this comprehensive exposition delves into the far-reaching implications for optimal load management and resource allocation, magnifying the transformative potential that AI-driven algorithms hold in precisely balancing energy utilization and deftly managing the intricate interdependencies that permeate load distribution. Through meticulous elucidation, this illuminating manuscript emboldens the reader with insights into the progressive advancements and myriad benefits that the tandem of AI and ML confers upon the dynamic energy sector, charting an unyielding course towards unprecedented resilience and sustainable utilization of our cherished renewable energy resources.

Medium access in cloud-based for the internet of things based on mobile vehicular infrastructure

Smart cities are made up of a large number of smart, intelligent gadgets that can sense, compute, act, and communicate. Focusing on how data is transferred between sensory devices and applications in the internet of things (IoT), and cyber-physical systems have led to 5G/IoT integration. This paper proposes a revolutionary architecture for mobile vehicular cloud infrastructure that takes variable weather, road, and traffic circumstances into consideration. It proposes a dynamic speed management system for smart cities. To optimize system flexibility and reduce costs, the system makes advantage of the most recent advancements in wireless communication and utilizes current telecommunication infrastructures utilized in data streaming, sound, and video. The study presents an internet protocol (IP) real-time subsystem-network-based framework for requesting bandwidth from free wireless channel resources using the channell quality indicator channel.

Exploring Self-Healing and Switchable Adhesives based on Multi-Level Dynamic Stable Structure.

It is a challenge to develop adhesives simultaneously capable of strong adhesion and efficient switchable ability. Herein, the authors report multifunctional switchable adhesives named Cu2+ -curcumin-imidazole-polyurethane (CIPUs:Cu2+ ) by introducing 1-(3-aminopropyl) imidazole and curcumin into polyurethane system crossed by Cu2+ forming dynamic metal-ligand bonds. This CIPUs:Cu2+ has strong adhesion (up to 2.46MPa) on various material surfaces due to their specially designed functional groups alike the secretions from mussels. It can achieve fast switching speed (30 s) and high switch efficiency through multiple contactless remote stimulations. Importantly, density functional theory (DFT) calculation reveals that such metal-ligand bonds consisting of two components: stronger Cu2+ -curcumin complexes and weaker Cu2+ -imidazole complexes can aggregate to form multi-level dynamic stable structure . The special structure can not only be acted as sacrificial sites for easily broken and reformed, allowing efficient switchable adhesion and enormous energy dissipation but also acted as firm sites to maintain the cohesion of the adhesive and the reversible reconstruction network. Intriguingly, the CIPUs:Cu2+ can achieve self-healing at room temperature without needing external stimuli. Overall, this strategy can further broaden the design of switchable adhesives in the fields of intelligent gadgets, wearable bio-monitoring devices, etc.

Machine learning based IoT system for secure traffic management and accident detection in smart cities.

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

AI-based Cybersecurity Attacks and Countermeasures in IoT Environment: A Survey

The Internet of Things (IoT) has been considered an innovative integrated solution that opened opportunities for intelligent appliances to communicate over the Internet. However, IoT suffers from various cybersecurity threats because of its openness and the constraints of the used devices that share substantial amounts of classified information. Artificial intelligence (AI) is an effective key technology in solving cybersecurity issues. Cybercriminals are adopting AI techniques to conduct sophisticated attacks on IoT environments. Precise and elusive attacks across various applications have exemplified the deliberate employment of artificial intelligence for malicious intentions. Threat actors are constantly changing and improving their attack strategies with particular emphasis on applying AI-driven techniques in the attack process, called AI-based cybersecurity attacks, which can be used with conventional attack techniques to cause considerable damage. Moreover, AI techniques can be utilized in cyber-attack countermeasures and have proven effective. Unfortunately, these types of attacks and countermeasures have not been investigated enough in the existing literature. This paper surveys the current studies of AI-based cybersecurity attacks and countermeasures in the IoT environment. The aspects captured by this paper highlight future research in this field and will assist in recognizing the power and impact of using AI in cybersecurity, especially in the IoT environment.

Early alert for sleep deprivation using mobile sensor data fusion

In recent years, with the widespread use of digital technology and intelligent gadgets, an enormous amount of data are being generated at an alarmingly rapid rate. The quickly growing technologies and data generated by mobile and Internet of Things devices are tremendous. These data can be used to achieve sleep deprivation in users using machine learning. Multi-modal sensors and technology to detect activity, sleep, and circadian rhythms have become increasingly widespread. For the first time, large-scale sleep tracking is conceivable. In addition to large-scale epidemiological research, multi-sensor data can be used for wellness applications such as sleep coaching for chronic disease patients. Individuals, health, and research must overcome several fundamental obstacles before new technologies may ultimately benefit. We examine existing sleep-monitoring technology from data gathering through mobile data sensors to machine learning mode. We also analyze the current and emerging sensing technologies.

Medical Data Integration and Interoperability through Remote Monitoring of Healthcare Devices

In the age of intelligent gadgets and interconnected communities, the widespread surveillance and provision of healthcare to patients are made feasible through the Internet of Medical Things (IoMT). The number of implanted electronic devices that can be monitored remotely is increasing, leading to a rise in the amount and intricacy of biological data. The collected data can offer valuable diagnostic information that can be used to intervene and maintain implanted devices promptly, therefore enhancing the quality of treatment provided. Current remote monitoring processes are not fully using device diagnostics because of the lack of compatibility and data integration between exclusive applications and Digital Health Record (DHR) platforms. Establishing a technology framework that establishes information and enhances interoperability has the potential to strengthen remote monitoring. This article introduces the Medical Data Integration and Interoperability via Remote Monitoring of Healthcare Devices (MDII-RMHD) framework, which facilitates the collaboration of healthcare devices. MDII-RMHD enhances a cloud-based IoMT system using conversion resources at the network's boundary. This is achieved through the use of inquiring and conversion agents. The investigating devices keep track of a list of MDII-RMHD devices on the regional network and allow one device to seek information conversion from another gadget when the requesting device cannot do the task independently. The converting agent transforms the data into the necessary format and returns it to the original entity. These agents enable IoMT devices to utilize their surplus computational capabilities for data translations, reducing the need for cloud access. Conventional devices are compatible with fog resource managers that have MDII-RMHD support. We assess the MDII-RMHD framework by rigorous experimentation under different scenarios. The obtained results demonstrate that the proposed framework decreases the amount of uplink traffic and enhances the response time. This improvement in response time is significant for real-time healthcare applications.

Identity and Access Management for The Internet of Things (IoT)

The ease with which intelligent gadgets may connect to the internet is a significant factor contributing to their rapid increase in popularity. The increasing popularity of smart gadgets is attributed to numerous factors, including their ability to introduce various unexplored opportunities in multiple industries. User ID administration should be more attention when developing Internet of Things platforms. Recently, behavior has shifted as more individuals are embracing technologies that enable devices to be recognized and connected to platforms associated with the Internet of Things. This method aims to facilitate the integration of more fundamental security designs during the development of applications for the Internet of Things. Consequently, identity and access management have been swiftly embraced in several IoT business sectors. Ensuring the continuous protection of user information is the sole viable solution, albeit an unfavorable one.

Motion Activated, Temperature Controlled (Matc) Electric Fan

The major objective of the study was to design and develop a motion activated and temperature-controlled electric fan. It is the most practical and affordable way of keeping areas cool especially during hot and humid months. In the Bulacan State University, many electric fans installed in lecture rooms are left unattended even after class hours. This translates to unnecessary fire risks and/or excessive electric consumption of the University. It is in this light that the researchers developed a device that presents an opportunity for the development of intelligent appliances that can save on electricity consumption due to its innovative and eco-friendly features. Various designs were put forward and a careful consideration of each plan was done, with special considerations to the need for innovative appliances that can be very useful in terms of energy efficiency and environmentfriendliness through an integrated multi-dimensional sensor system. The study utilized the developmental research method in the fabrication of the project. The focus on the procedures and processes involved analysis, development and evaluation of the project and in validating the project. The steps involved in this method include the following: (1) studying research findings pertinent to the project; (2) developing the project based on the findings; (3) field testing of the project, and (4) revising the project. The project was developed in sixty (60) working days, where prototypes were subjected to a dry run and simulation period of two (2) weeks continuous operation. The actual operation cycles of the prototypes in accordance with the varying room temperatures throughout the day was also observed during this period, testing them on a varied number of parameters, namely: operation time, turn on times in terms of fan speed control mode and motion sensitivity tests. Evaluation results revealed that the project functioned according to its lofty expectations where it was determined that it was the appropriate design that integrated the most practical and adaptable configuration for the intended application. It is in this line that intelligent or smart ventilation systems have now matured, providing a cost-effective approach to managing hot and humid conditions.

Motion Activated, Temperature Controlled (Matc) Electric Fan

The major objective of the study was to design and develop a motion activated and temperature-controlled electric fan. It is the most practical and affordable way of keeping areas cool especially during hot and humid months. In the Bulacan State University, many electric fans installed in lecture rooms are left unattended even after class hours. This translates to unnecessary fire risks and/or excessive electric consumption of the University. It is in this light that the researchers developed a device that presents an opportunity for the development of intelligent appliances that can save on electricity consumption due to its innovative and eco-friendly features. Various designs were put forward and a careful consideration of each plan was done, with special considerations to the need for innovative appliances that can be very useful in terms of energy efficiency and environmentfriendliness through an integrated multi-dimensional sensor system. The study utilized the developmental research method in the fabrication of the project. The focus on the procedures and processes involved analysis, development and evaluation of the project and in validating the project. The steps involved in this method include the following: (1) studying research findings pertinent to the project; (2) developing the project based on the findings; (3) field testing of the project, and (4) revising the project. The project was developed in sixty (60) working days, where prototypes were subjected to a dry run and simulation period of two (2) weeks continuous operation. The actual operation cycles of the prototypes in accordance with the varying room temperatures throughout the day was also observed during this period, testing them on a varied number of parameters, namely: operation time, turn on times in terms of fan speed control mode and motion sensitivity tests. Evaluation results revealed that the project functioned according to its lofty expectations where it was determined that it was the appropriate design that integrated the most practical and adaptable configuration for the intended application. It is in this line that intelligent or smart ventilation systems have now matured, providing a cost-effective approach to managing hot and humid conditions.

Switchable Adhesion: On-Demand Bonding and Debonding.

Adhesives have a long and illustrious history throughout human history. The development of synthetic polymers has highly improved adhesions in terms of their strength and environmental tolerance. As soft robotics, flexible electronics, and intelligent gadgets become more prevalent, adhesives with changeable adhesion capabilities will become more necessary. These adhesives should be programmable and switchable, with the ability to respond to light, electromagnetic fields, thermal, and other stimuli. These requirements necessitate novel concepts in adhesion engineering and material science. Considerable studies have been carried out to develop a wide range of adhesives. This review focuses on stimuli‐responsive material‐based adhesives, outlining current research on switchable and controlled adhesives, including design and manufacturing techniques. Finally, the potential for smart adhesives in applications, and the development of future adhesive forms are critically suggested.

Design of a Small Sweeping Robot Based on STM32

With the continuous improvement of people’s living standards and the emergence of various intelligent appliances, a household floor sweeping robot is designed in this subject, which can automatically clean the ground in the room. The robot takes STM32F103ZET6 single chip microcomputer as the controller, uses two DC motors and L298N driving chips to drive the robot to walk, and uses ultrasonic sensors to detect indoor obstacles, The infrared sensor is used to detect the indoor edge position, the straight travel control of the robot is realized through the coding disk combined with PID algorithm, and the turning control is realized through differential control and PWM speed regulation. The robot can realize the garbage cleaning in the regular room, and has a certain application prospect.

Demand Side Management Techniques for Peak Reduction

Industrial growth is the back bone for the development of any nation. Industries are mainly dependent on electrical energy. But from the various studies, the sources for electrical energy are decreasing gradually, and in turn, the gap is increasing between the supplier and the load. The solution for this scenario is optimal utilization of resources. To overcome this problem , the concept Demand Side Management (DSM) has emerged in Power System Planning and Management. The principle objective of DSM is mutual understanding between the supplier and the consumer for maximizing benefits and minimizing inconvenience. The aim of this research work is selection and application of appropriate DSM techniques to industrial and domestic loads for peak load management and energy conservation, that is to control the maximum demand during the peak hours and saving the energy by using the energy efficient and intelligent appliances like air conditioners and water heaters. DSM includes techniques like the End Use Equipment Control, the Load Priority Technique, he Peak Clipping & Valley filling, the Differential Tariff and Resizing of the equipment. Depending upon the application, all the techniques may be applied sequentially, or only a few of them can be applied. There is a lot of ambiguity in the selection of DSM techniques, because the application of each DSM technique depends on the case study and the problem associated with the respective case study. After comprehensive understanding of a particular case, a thorough investigation and subsequent data analysis pave the way for the selection of appropriate DSM technique/techniques

Early Warning System for Possible Heart Attacks Using IOT

Now-a-days, diseases related to heart are mounting at a very great speed. Due to these problems, timely health check up has become vital. A modern concept of health monitoring a patient includes wirelessly monitoring. It is a major development in medical arena. This system depends on doctor examining patient continuously without patient being physically present for check up. In medical science, excellent and cost effective gadgets related to monitoring health utilizing popular technologies such as wireless communications and portable remote health monitoring device are developed. This development provides ease to the patients having various diseases. As a result, visit of patients to the doctors constantly has lowered because various types of reports can be generated remotely and wirelessly at doctor’s end at regular interval of time. Because of this recent development in scientific technology, doctors are saving several lives. Internet of Things provides platform for development of many intelligent gadgets & applications. IoT infrastructure provides base of connectivity and technology. IoT intelligent devices can implement the facilities of monitoring health remotely and notifying any health issues in emergency. In this paper, we propose a design for transfer of personal medical information and facility for storing the same over cloud infrastructure. The hardware devices like Raspberry Pi, Heartbeat sensor, Blood Pressure sensor, SpO2 sensor, Body Temperature sensor will be used in this system. Raspberry Pi will perform dual role of being a sensor node as well as system controller.