Aadam Quraishi's Books

IOT SECURITY: SECURING THE INTERNET OF THINGS DEVICES AND NETWORKS

By: Dr. Aadam Quraishi MD,Mr. Shashishekhar Ramagundam,Dr. Vaibhav Bhatnagar,Prof. Reeta Singh

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IOT SECURITY: SECURING THE INTERNET OF THINGS DEVICES AND NETWORKS

By: Dr. Aadam Quraishi MD,Mr. Shashishekhar Ramagundam,Dr. Vaibhav Bhatnagar,Prof. Reeta Singh

Organizations are basically required to be completely satisfied with the security risks before integrating Internet of Things (IoT) in an existing system or constructing an entirely new system. This is the case regardless of whether the system is being developed from scratch or already in existence. As a consequence of this, the parties who offer solutions for the Internet of Things have a significant amount of trouble in establishing their reputation in the field of technology. Because every business has its own distinct approach to visualizing and conceptualizing the deployment of the Internet of Things, this leads to a rise in anxiety and a lack of trust in the appropriateness of security measures. Most of the suppliers are more concerned with the solutions that they are able to provide to the organization through the pool of sensors, data collection and analysis servers, and optimization subroutines. This is because the majority of the suppliers are capable of providing these solutions. The deployment of the system has resulted in a noticeable decrease in the level of worry that they exhibit with regard to the potential threats to their security, which is a more serious issue. Simply offering an organization with a bespoke suite of electrical components that are compatible with software services in the context of Internet of Things deployment is not adequate for the business that is seeking to update its technology. Each and every Internet of Things vendor is aware that security has been the primary concern of organizations over the course of the past few years. As a result, they are required to provide an Internet of Things solution that is equipped with secure and dependable operations by utilizing a variety of firewalls and security protocols. All Internet of Things vendors are aware of this reality. Nevertheless, there is no general security phenomena that they can use to educate their consumers about security issues; rather, it would require a more individualized approach with security constraints that are suited to the unique demands of the client. Therefore, in order to make the Internet of Things (IoT) more effective, the business needs to have faith in it and rely on it firmly. This is something that can only be performed once the vendors

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190

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Xoffencerpublication

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ISBN 10

811953493X

ISBN 13

9788119534937

MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

By: Dr. Faisal Alghayadh,Dr. Aadam Quraishi,Mr. Sivasubramanian,Dr. Rabinarayan Patnaik

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MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

By: Dr. Faisal Alghayadh,Dr. Aadam Quraishi,Mr. Sivasubramanian,Dr. Rabinarayan Patnaik

Within the ever-evolving realm of artificial intelligence (AI), the field of Machine Learning Interpretability (MLI) has surfaced as a crucial conduit, serving as a vital link between the intricate nature of sophisticated AI models and the pressing necessity for lucid decision-making procedures in practical scenarios. With the progressive integration of AI systems across various domains, ranging from healthcare to finance, there arises an escalating need for transparency and accountability concerning the operational mechanisms of these intricate models. The pursuit of interpretability in machine learning is of paramount importance in comprehending the enigmatic essence of artificial intelligence. It provides a structured methodology to unravel the intricate mechanisms of algorithms, thereby rendering their outputs intelligible to human stakeholders. The Multimodal Linguistic Interface (MLI) functions as a pivotal conduit, bridging the dichotomous domains of binary machine intelligence and the intricate cognitive faculties of human comprehension. Its primary purpose lies in fostering a mutually beneficial association, wherein the potential of artificial intelligence can be harnessed with efficacy and conscientiousness. The transition from perceiving AI as a "black box" to embracing a more transparent and interpretable framework represents a significant paradigm shift. This shift not only fosters trust in AI technologies but also empowers various stakeholders such as end-users, domain experts, and policymakers. By gaining a deeper understanding of AI model outputs, these stakeholders are equipped to make informed decisions with confidence. In the current epoch characterized by remarkable progress in technology, the importance of Machine Learning Interpretability is underscored as a pivotal element for the conscientious and ethical implementation of AI. This development heralds a novel era wherein artificial intelligence harmoniously interfaces with human intuition and expertise

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239

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Xoffencerpublication

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ISBN 10

8119534913

ISBN 13

9788119534913

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

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INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

The capacity to understand and have trust in the results generated by models is one of the distinguishing characteristics of high-quality scientific research. Because of the significant impact that models and the outcomes of modeling will have on both our work and our personal lives, it is imperative that we have a solid understanding of models and have faith in the results of modeling. This is something that should be kept in mind by analysts, engineers, physicians, researchers, and scientists in general. Many years ago, picking a model that was transparent to human practitioners or customers often meant selecting basic data sources and simpler model forms such as linear models, single decision trees, or business rule systems. This was the case since selecting a model that was transparent required less processing power. This was the situation as a result of the fact that picking a model that was transparent to human practitioners or customers in general entailed picking a model. Even though these more easy approaches were typically the best option, and even though they continue to be the best option today, they are subject to failure in real-world circumstances in which the phenomena being replicated are nonlinear, uncommon or weak, or very distinctive to particular individuals. Despite the fact that they continue to be the best option, they are sensitive to failure in these kinds of scenarios. The conventional trade-off that existed between the precision of prediction models and the simplicity with which they could be interpreted has been abolished; nevertheless, it is likely that this trade-off was never truly required in the first place. There are technologies that are now accessible that can be used to develop modeling systems that are accurate and sophisticated, based on heterogeneous data and techniques for machine learning, and that can also aid human comprehension of and

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268

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Xoffencerpublication

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ISBN 10

8119534670

ISBN 13

9788119534678

MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

By: Bhargavi Posinasetty,Dr. Aadam Quraishi MD,Dr. Vikas Malgotra,Mr. Dayakar Babu Kancherla

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MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

By: Bhargavi Posinasetty,Dr. Aadam Quraishi MD,Dr. Vikas Malgotra,Mr. Dayakar Babu Kancherla

In the discipline of healthcare informatics, the study of how data relevant to healthcare may be obtained, transmitted, processed, stored, and retrieved is known as the study of how data can be gathered, transferred, processed, stored, and retrieved. In this area of study, early sickness prevention, early illness detection, early illness diagnosis, and early illness therapy are all crucial components. The only kinds of data that are regarded credible in the field of healthcare informatics are those that pertain to diseases, patient histories, and the computer operations that are necessary in order to analyze this data. Over the course of the past two decades, conventional medical practices across the United States have made major investments in cutting-edge technical and computational infrastructure in order to enhance their capacity to provide assistance to academics, medical professionals, and patients. There has been a significant investment of resources made in order to improve the level of medical care that may be offered by utilizing these various. The motivation for all of these many programs was the overarching goal of providing patients with access to healthcare that is not only affordably priced and of high quality, but also totally and entirely free of any and all fear. As a direct result of these efforts, the benefits and relevance of employing computational tools to aid with referrals and prescriptions, to set up and maintain electronic health records (EHR), and to make technical improvements in digital medical imaging have been clearer. This is a direct result of the fact that the advantages of applying computational tools have become more obvious. Electronic health records (EHR) are something that can be set up and managed with the assistance of these technologies. It has been demonstrated that computerized physician order entry, more frequently referred to as CPOE, may be able to increase the quality of care that is delivered to patients while

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268

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Xoffencerpublication

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ISBN 10

8119534778

ISBN 13

9788119534777

SUPERVISED LEARNING ALGORITHMS CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Aadam Quraishi,ANIL WURITY

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SUPERVISED LEARNING ALGORITHMS CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Aadam Quraishi,ANIL WURITY

The branch of computer science known as machine learning is one of the subfields that is increasing at one of the fastest rates now and has various potential applications. The technique of automatically locating meaningful patterns in vast volumes of data is referred to as pattern recognition. It is possible to provide computer programs the ability to learn and adapt in response to changes in their surroundings via the use of tools for machine learning. As a consequence of machine learning being one of the most essential components of information technology, it has therefore become a highly vital, though not always visible, component of our day-to-day life. As the amount of data that is becoming available continues to expand at an exponential pace, there is good reason to believe that intelligent data analysis will become even more common as a critical component for the advancement of technological innovation. This is because there is solid grounds to believe that this will occur. Despite the fact that data mining is one of the most significant applications for machine learning (ML), there are other uses as well. People are prone to make mistakes while doing studies or even when seeking to uncover linkages between a lot of distinct aspects. This is especially true when the analyses include a large number of components. Data Mining and Machine Learning are like Siamese twins; from each of them, one may get a variety of distinct insights by using the right learning methodologies. As a direct result of the development of smart and nanotechnology, which enhanced people's excitement in discovering hidden patterns in data in order to extract value, a great deal of progress has been achieved in the field of data mining and machine learning. These advancements have been very beneficial. There are a number of probable explanations for this phenomenon, one of which is that people are currently more inquisitive than ever before about identifying hidden patterns in data. As the fields of statistics, machine learning, information retrieval, and computers have grown increasingly interconnected, we have seen an increase in the led to the development of a robust field that is built on a solid mathematical basis and is equipped with extremely powerful tools. This field is known as information theory and statistics. The anticipated outcomes of the many different machine learning algorithms are culled together into a taxonomy that is used to classify the many different machine learning algorithms. The method of supervised learning may be used to produce a function that generates a mapping between inputs and desired outputs. The production of previously unimaginable quantities of data has led to a rise in the degree of complexity shown across a variety of machine learning strategies. Because of this, the use of a great number of methods for both supervised and unsupervised machine learning has become obligatory. Because the objective of many classification challenges is to train the computer to learn a classification system that we are already familiar with, supervised learning is often used in order to find solutions to problems of this kind. The goal of unearthing the accessibility hidden within large amounts of data is well suited for the use of machine learning. The ability of machine learning to derive meaning from vast quantities of data derived from a variety of sources is one of its most alluring prospects. Because data drives machine learning and it works on a large scale, this goal will be achieved by decreasing the amount of dependence that is put on individual tracks. Machine learning functions on data. Machine learning is best suited towards the complexity of managing through many data sources, the huge diversity of variables, and the amount of data involved, since ML thrives on larger datasets. This is because machine learning is ideally suited towards managing via multiple data sources. This is possible as a result of the capacity of machine learning to process ever-increasing volumes of data. The more data that is introduced into a framework for machine learning, the more it will be able to be trained, and the more the outcomes will entail a better quality of insights. Because it is not bound by the limitations of individual level thinking and study, ML is intelligent enough to unearth and present patterns that are hidden in the data.

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210

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Xoffencerpublication

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ISBN 10

8119534336

ISBN 13

9788119534333

BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES

By: Dr. Gargishankar Verma,Aadam Quraishi,Ismail Keshta,Dr. Haewon Byeon

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BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES

By: Dr. Gargishankar Verma,Aadam Quraishi,Ismail Keshta,Dr. Haewon Byeon

The Internet of Things (IoT) is a technology that enables a network of physical items (things) to sense physical events, transmit data, and interact with their environment in order to make decisions or monitor certain processes and occurrences without the need for human contact. This may be accomplished through the use of the internet. The desire to make it simpler to collect data in real time and to offer automatic and remotecontrol mechanisms as a substitute for the conventional monitoring and control systems used in many sectors today was one of the most significant reasons for the development of IoT systems. This goal has been one of the most important reasons for the development of IoT systems. Manufacturing, environmental monitoring, digital agriculture, smart cities and homes, business management, and asset tracking are some of the sectors that fall under this category. It is expected that the number of devices that are connected to one another will have topped 20 billion by the year 2020. Because of these growing demands and the huge penetration of IoT across a wide variety of rising industries, quick innovation in the existing IoT protocols, technologies, and architectures is necessary, as well as significant work to define IoT standards that will enable these developments. The Internet of Things (IoT) generates large volumes of data, which demands the availability of network connectivity as well as power, processing, and storage resources in order to transform this data into information or services that have any value. When implementing IoT networks, it is vital to emphasize cybersecurity and data privacy in addition to guaranteeing consistent connections and the scalability of the network. Other important considerations include ensuring that the network can be expanded. At the moment, centralized architectural models are utilized in an extensive manner to authenticate, authorize, and link the numerous nodes that make up an Internet of Things network. Moreover, these models are used to represent the Internet of Things. Because there will be a rising number of devices, which might reach hundreds of billions, centralized systems will break down and fail when the centralized server is not accessible. As a potential answer to this issue, a decentralized architecture for the Internet of Things was proposed. This design relocates some of the processing tasks that occur within the network to the periphery of the network.

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207

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Xoffencerpublication

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ISBN 10

8119534840

ISBN 13

9788119534845

MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

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MACHINE LEARNING FOR CYBER SECURITY DETECTING ANOMALIES AND INSTRUSIONS

By: Dr. Aadam Quraishi,Vikas Raina,Aditya Mahamkali,Arijeet Chandra Sen

Because the Internet is so widespread in modern life and because of the expansion of technologies that are tied to it, such as smart cities, self-driving cars, health monitoring via wearables, and mobile banking, a growing number of people are becoming reliant on and addicted to the Internet. In spite of the fact that these technologies provide a great deal of improvement to individuals and communities, they are not without their fair share of concerns. By way of illustration, hackers have the ability to steal from or disrupt companies, therefore inflicting damage to people all across the world, if they exploit weaknesses. As a consequence of cyberattacks, businesses can face financial losses as well as damage to their reputation. Consequently, the security of the network has become a significant concern as a result. Organizations place a significant amount of reliance on tried-and-true technologies such as firewalls, encryption, and antivirus software when it comes to securing their network infrastructure. Unfortunately, these solutions are not completely infallible; they are merely a first line of security against malware and other sophisticated threats. Therefore, it is possible that certain persons who have not been sanctioned may still get access, which might result in a breach of security. For the purpose of preventing intrusion detection, computer systems need to be safeguarded against both illegal users, such as hackers, and legitimate users, such as insiders. A breach of a computer system may result in a number of undesirable results, including the loss of data, restricted access to internet services, the loss of sensitive data, and the exploitation of private resources. an initial version of the Intrusion Detection System (IDS) was constructed. In light of the fact that it is a that is essential for the protection of computer networks, it has therefore become a subject of study that is widely pursued. Given the current condition of cybercrime, it is impossible to deny the significance of the intrusion detection system (IDS). A possible example of how the IDS taxonomy is arranged may be found here. The intrusion detection system, often known as an IDS, is a piece of software or hardware that monitors a computer or network environment, searches for indications of intrusion, and then notifies the user of any potential threats. Utilizing this warning report is something that the administrator or user may do in order to repair the vulnerability that exists inside the system or network. In the aftermath of an intrusion, it may be purposeful or unlawful to attempt to access the data

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