The Future of Computing: Ubiquitous Applications and Technologies

During lab tests, thousands of medical images are generated to trace the disease's symptoms. Manual interpretation of this data may consume excessive time and thus may delay diagnosis. Timely detection of critical diseases is very important as their stage can be changed over an interval. Automated analysis of medical data can reduce the gap between disease detection and its diagnosis and it also reduces the overall computational cost. In this paper, this goal will be achieved using different methods (Classification/ Segmentation/ Image Encoding/ Decoding/ Registration/ Restoration/ Morphology).

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized various industries, and the power sector is no exception. Smart Grid, an advanced power system that employs IoT devices and AI algorithms, promises enhanced efficiency, reliability, and sustainability. However, the proliferation of IoT devices in Smart Grid introduces new security challenges that must be addressed to ensure the integrity and privacy of critical infrastructure. This chapter aims to propose an IoT semantic of AI security structure for the Smart Grid, leveraging advanced AI techniques to detect and mitigate security threats effectively.

The true driving force for the creation of a federation of clouds is a few fundamental characteristics, including the variety of infrastructures, interfaces, and different aims. The federation must ensure the application of certain standards and interfaces that allow secure and effective communication across heterogeneous entities in order to ensure seamless and helpful interaction between diverse components or entities of the various cloud providers. The federation has many commercial, legal, and technical aspects to focus on. Major features like resource provisioning, security, monitoring, etc. are suggested differently in various types of federations. This chapter analyzes a number of federation architectures on various important parameters with a view to highlighting their effect on participating cloud providers. Aspects related to Service Level Agreement management, QoS, Security, and Scheduling are also discussed in the same comparison framework.

Improving digital payment trends in rural India is crucial given the growing impact of ICT penetration, demonetization, and digital activities for small businesses in rural sectors. The shift to digital payments can offer benefits such as transaction transparency, reducing parallel economy, and improving ease of doing business. Although various digital wallets such as Paytm, Mobikwik, and PhonePe have been introduced and the government has launched UPI solutions like the BHIM app, rural banking consumers still struggle to embrace digital payments due to the lack of digital literacy. India has a large rural population, but only a small percentage is digitally literate, hindering digital payment adoption. This research study examines the significance of digital literacy in the current banking environment, focusing on issues, opportunities, and difficulties related to the adoption of digital payments in the rural banking sector.

DC motor demand is rising in the industrial sector due to its efficiency and in contrast to AC motors, a DC motor's momentum can be easily adjusted. For industrial uses, making a highly regulated motor is essential. DC motors need to have excellent speed tracing and load regulation in order to operate satisfactorily. The speed of a DC motor was controlled in this work using proportional integral derivative (PID) controllers. This study used MATLAB to determine how a Proportional-IntegralDerivative (PID) controller affected the performance of a DC motor of the industrial type by selection of PID controller parameters using Zeigler’s Nichols (ZN), Genetic Algorithm (GA), and Fuzzy Inference System. Nonlinearities and model uncertainties must be included in the control design in order to provide effective and efficient control. The higher-order systems could use the suggested strategies. The PID controller's primary function is to regulate motor speed based on incoming system data and auto-tuning. The findings of the simulation also demonstrate improved motor performance, which decreases rise time, steady state error, and overshoot, and increases system stability.

Medical images contain data related to the diseases and it should be interpreted accurately. However, its visual interpretation is quite complex/timeconsuming and only medical experts can examine this data precisely. In case of diabetes, the retina may be damaged and it is quite complex to examine its impact on the retina because there are a lot of vessels inside the human eyes that may be changed due to this disease and manual interpretation of these changes consumes excessive time. In order to overcome this issue, in this paper, a contour-based pattern recognition method (CBPR) is introduced that can recognize multiple patterns in sample retina images. Comparative analysis with the segmentation-based method (SBPR) shows that it outperforms in terms of performance parameters (i.e. Accuracy/Sensitivity/ Specificity etc.). 

This research paper proposes a versatile standalone, cost-effective smart home system that does not require any substantial changes to the existing framework. The project is built with Arduino Uno and NodeMCU (ESP8266) microcontrollers that operate two distinct 4-channel Relays, which in turn control household appliances. Ubiquitous computing, also known as pervasive computing, is a computer science term that refers to the ability to be present everywhere and at any time. According to this notion, a user may interact with computers, which may exist in many forms such as laptops, tablets, and terminals in everyday items. To demonstrate the feasibility and efficacy of the proposed smart home system, devices such as LED lights, power connectors, and a fan have been integrated into the system. The NodeMCU is programmed using the Arduino IDE. It is linked to the Internet, where it receives signals and carries out the user-programmed actions on the relay. By clicking a button on the mobile application's interface, this function enables users to manually control all of their home appliances.

Over the past decade, advances in hardware, software, and networking have led to the evolution of modern-day smart devices, which are no longer simply mobile phones, but have significant computing power. Such a phenomenal increase in the performance and capabilities of smartphones, tablets, and personal digital assistants, along with the convenience of using them, has practically led them to replace computers and notebooks. However, their small size makes them susceptible to theft. Also, the data they contain coupled with continuous network connectivity makes them susceptible to malicious activities and attacks. Investigation of such incidents as well as the increasing technical difficulties in extracting evidence from mobile devices has resulted in the emergence of mobile forensics within the digital forensics discipline. Mobile forensics is specialized in retrieving and processing evidence from mobile devices such that it is admissible in a court of law. While the scope of mobile forensics includes advanced evidence analysis and threat intelligence to thwart attacks or malicious activities, data acquisition still remains its main focus. This paper presents an overview of the research conducted in the domain of forensic acquisition of mobile phones during the past decade, identifying the challenges and opportunities in the field.

The Internet of Things (IoT) has rapidly gained popularity as a technology that enables devices to communicate with each other and the Internet, opening up a world of possibilities for new applications and services. This chapter provides an overview of IoT, its applications, and the challenges that need to be addressed in its deployment. IoT and AIoT are two of the most significant technological innovations of the 21st century. IoT allows physical devices to connect and exchange data, while AIoT enables these devices to learn, analyze, and make decisions based on the data they collect. The term “AIOT” stands for “Artificial Intelligence of Things.” AIOT refers to the integration of Artificial Intelligence (AI) technologies with the Internet of Things (IoT) ecosystem. In essence, AIOT combines the capabilities of AI and IoT to create intelligent, self-learning systems that can analyze, interpret, and respond to data generated by IoT devices. Together, these technologies offer numerous benefits such as increased efficiency, better decision-making capabilities, and improved outcomes across industries like healthcare, manufacturing, transportation, and agriculture. As more devices and systems become connected, IoT and AIoT will continue to play a critical role in shaping the future of our world. IoT and AIoT have the potential to transform the way we live and work. By enabling devices to communicate and share data, IoT can help us create more efficient and effective systems, and by integrating AI technologies, IoT devices can become smarter and more autonomous. This means that devices can analyze data in real-time, make decisions, and adapt to changing conditions without human intervention. For example, in smart cities, IoT and AIoT can help reduce traffic congestion by optimizing traffic flows, and in healthcare, they can help monitor patients remotely and alert healthcare providers when necessary. As more devices and systems become connected, we can expect IoT and AIoT to become increasingly sophisticated, offering new opportunities for innovation and growth in various industries. However, as with any new technology, there are also potential risks and challenges that must be addressed, such as security and privacy concerns, and the need for new regulations and standards to ensure the safe and ethical use of these technologies.