The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Computers are part of our everyday life. Advances in technology allow these machines to duplicate human skills with remarkable accuracy. AI managed all global business functions. Progress is mostly due to AI. Using AI, computers can automate human work. Many organizations employ AI to simplify. Business uses it extensively. Companies use AI to automate, analyze, and interact with consumers and workers. Companies want market domination and industry growth. Business insights are gained by many successful global firms using AI, automation, big data analytics, and NLP. AI is transforming business by automating processes, analyzing data, improving decision-making, and connecting customers. AI may improve corporate chatbots, inventory management, fraud protection, and predictive maintenance. AI can raise productivity, cut expenses, and increase profits by speeding up procedures and enhancing customer service. However, businesses must use AI responsibly and protect data. This chapter discusses AI's business impacts. 

Artificial Intelligence at the Helm

Due to technological advances in artificial intelligence (AI), the public and academic library community are interested in using it. Since this technology is still being researched and has not yet been made available to the public, there is a unique opportunity to study a representative sample of librarians using the diffusion of innovations model and their views on artificial intelligence. There is no scientific library and information science (LIS) publication that covers this breadth yet. Such an analysis might provide light on innovation diffusion theory and academic library personnel' views on AI in general. This research may be valuable for technologically oriented academic library administrators and personnel who want to promote artificial intelligence technologies in their libraries. 

Integrating Artificial Intelligence in Library Management

With climate change and natural catastrophe risk rising, India faces several risks. Administrators, disaster responders, and medical teams face difficult terrain. Emergency decision-making is being pursued to increase natural catastrophe resilience. Artificial intelligence and machine learning can revolutionize emergency decision-making by providing data-driven insights, improving predictive capabilities, optimizing resource allocation, and strengthening communities facing India's rising disaster risk. Artificial intelligence and machine learning can quickly and efficiently analyze large datasets during crises due to the turmoil and flood of enormous data from varied sources. Advanced tools for real-time data analysis and predictive modeling are needed to help disaster management decision-makers traverse the complex labyrinth. In this chapter, technology and resilience are examined in depth. We've seen AI and machine learning alter emergency decision-making, demonstrating its adaptability and efficacy. 

Strengthening Resilience

Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites and weather stations. AI may help catastrophe management. AI reduces disaster risk in many ways. Early warning systems, weather forecasts, disaster recovery, and reconstruction improve. AI could help us predict, prepare, and recover from natural calamities. These technologies provide climate change mitigation and community protection hope. They propose a better future amid climate change catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, and more. Growing usage of AI in disaster management raises questions about its benefits. We face what issues? How can these difficulties be resolved and opportunities maximised? What can AI tell policymakers, stakeholders, and the public to reduce disasters? The chapter introduces catastrophe management AI, AI implementation issues, and solutions to make the world more peaceful will be addressed. 

Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

Crucial attacks in internet of things via artificial intelligence techniques: The security survey

Implementation of blockchain in supply chain

Blockchain disruption in banking sector

The role of internet of things and artificial intelligence in smart health care

The COVID-19, novel coronavirus, is a pandemic and contaminated disease and it is aggressively increasing day by day from the first occurrence. Till now, within three months, a total of more than twenty lacks people affected with viruses, and over 50 thousand people have died. The virus is mainly attacking the respiratory system of the human body. The cases were increasing exponentially from day by day. And the diagnostic test of COVID-19 is to detect the presence of viral proteins in a sample from a person’s respiratory tract. But this process of testing is taking more time, and it is cost effective. For the prediction of COVID-19, we proposed a deep learning-based convolution neural network model using a chest X-ray of humans. The CNN model is trained on the dataset of COVID-19 X-ray and normal human X-ray; so it will able to identify the density of pneumonia in the human body to detect the COVID-19 virus.

COVID-19 and SARS Virus Predictions from Chest X-ray Images Using a Deep Learning Model

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