Dheerdhwaj Barak’s research while affiliated with Vaish College of Engineering and other places

The conductor gives the ticket in the traditional transportation system. The entire process is essentially paper-based and tickets are provided on printed papers. Both the amount of money received and the distance traveled by passengers are manually calculated. The cashless system, which makes use of QR Code, is widely used in several countries. In order to replace the manual fare collecting method and increase the efficiency of fare collection, the Transit Smart Card method, a new and innovative Automatic Fare collecting (AFC) System, is introduced in this work. The bus card or the QR reader can be used by passengers in place of a bus ticket. The QR scanner instructs the travelers on how to create an account, connect their band details to the app, and load funds into their wallet. The allocated and collected ticket fare is based on the user's selected destination. The passenger receives an SMS notification with a confirmation of the ticket payment. When a traveler arrives at their destination, the door will open if they scan their ticket to verify they have a ticket. Additionally, the printed materials will be reduced, and the loss of the card is also eliminated. It would ensure that tedious and financial problems like change are kept to a minimum. The current method of delivering Bus Tickets requires the passenger to wait for a long time before the stage closure and then queue to receive the pass. It also helps India become more digitalize.

The internet of things (IoT) and wireless sensor networks (WSNs) have become important innovations, offering unparalleled connectivity and pervasive data access for a variety of uses. They have combined to cause a paradigm change in the way we see and engage with our environment. But as connection has increased, new difficulties have emerged, largely concentrated around protecting the integrity of these networked systems. The cheap cost, long-term autonomy, and unsupervised operating capabilities of WSNs and IoT are the main reasons for their adoption. An enormous amount of data is now available for internet access due to the convergence of internet-enabled devices and sensor-driven data collecting, which has been facilitated by this integration. Users and network administrators are worried about the intrinsic security holes and vulnerabilities in these networks notwithstanding these advancements. WSNs and the internet of things are more susceptible to a wide range of threats because they lack a centralised security architecture. It becomes imperative to guarantee the confidentiality, integrity, and accessibility of data (CIA), particularly in applications where these characteristics are vital. In addition to the issues already listed, security risks are made worse by the intricacies brought about by the ever-changing dynamics of IoT ecosystems and the vast quantity of networked devices. New attack vectors appear as these systems develop, underscoring the need of carefully looking into any possible weaknesses. The significance of providing a thorough analysis of security risks, including both established and emerging attacks on WSNs and IoT, is emphasized in this study. This kind of analysis is necessary to classify and understand various assault types. Moreover, it underscores the need of addressing the intricate problems presented by WSN-IoT integration, including safeguarding communication protocols, overseeing extensive networks, and preserving data integrity over diverse devices and platforms. Establishing trust, reliability, and popular acceptance of these important technologies depends on recognizing those threats and safeguarding these linked systems from future assaults.

The current work discusses the concept of the internet of things (IoT) and its implications on various domains, highlighting the challenges and security concerns associated with its expansive scope. This review work emphasizes the need for comprehensive security solutions to address the complexities of IoT infrastructure, particularly in the context of emerging threats. This chapter also underscores the importance of integrating security, energy efficiency, software applications, and data analytics in IoT systems. It outlines the evolving landscape of IoT security, including the vulnerabilities and potential consequences of inadequate security measures. Additionally, authors address the intersection of security and privacy concerns within deep learning (DL) and machine learning (ML), discussing various strategies such as homomorphic encryption, differential privacy, trusted execution, and secure multiparty computing. It acknowledges the computational demands of these approaches and the ongoing search for globally harmonized solutions. Finally, authors conclude by highlighting the challenges and strategies in countering adversarial attacks in DL and ML, emphasizing the effectiveness of adversarial training and the multifaceted nature of defense mechanisms.

The Internet of Things (IoT) proposes to transform human civilization so that it is smart, practical, and highly efficient, with enormous potential for commercial as well as social and environmental advantages. Reliability is one of the major problems that must be resolved to enable this revolutionary change. The reliability issues raised with specific supporting technologies for each tier according to the layered IoT reliability are initially described in this research. The research then offers a complete review and assessment of IoT reliability. In this paper, various types of reliability on the IoT have been analyzed with each layer of IoT to solve the issues of failure rates, latency, MTTF, and MTBF. Each parameter has a certain classification and perception as well as enhancement in efficiency, accuracy, precision, timeliness, and completeness. Reliability models provide efficient solutions for different IoT problems, which are mirrored in the proposed study and classified with four types of reliabilities. The field of IoT reliability exploration is still in its initial phases, despite a sizable research record. Furthermore, the recent case study of CHISS is elaborated with discovered behaviors including brand-new aspects such as the multifaceted nature of evolving IoT systems, research opportunities, and difficulties.

The body's imbalanced glucose consumption caused type 2 diabetes, which in turn caused problems with the immunological, neurological, and circulatory systems. Numerous studies have been conducted to predict this illness using a variety of clinical and pathological criteria. As technology has advanced, several machine learning approaches have also been used for improved prediction accuracy. This study examines the concept of data preparation and examines how it affects machine learning algorithms. Two datasets were built up for the experiment: LS, a locally developed and verified dataset, and PIMA, a dataset from Kaggle. In all, the research evaluates five machine learning algorithms and eight distinct scaling strategies. It has been noted that the accuracy of the PIMA data set ranges from 46.99 to 69.88% when no pre-processing is used, and it may reach 77.92% when scalers are used. Because the LS data set is tiny and regulated, accuracy for the dataset without scalers may be as low as 78.67%. With two labels, accuracy increases to 100%.

Credit cards, mobile wallets, and other electronic payment methods are gaining popularity. Online transactions are increasingly the norm — global fraud increases as electronic payments increase. As credit cards and online shopping become increasingly popular, fraud has skyrocketed. Fraud detection and prevention are being prioritized due to the global economy.

Lung inflammation is caused by the development of cancer cells. As the frequency of cancer rises, men and women are dying at a higher rate. With malignancy, cancerous cells multiply uncontrollably in the lobes. It is impossible to prevent lung cancer, but we can lower its associated risks. Early detection of lung cancer can considerably improve a patient's chances of survival. Patients with lung disease are more likely to be chain smokers. Several classification methods were applied to assess lung cancer prediction, such as the deep CNN algorithm and deep CNN, with the final layer as machine learning. The first deep CNN model defined this accuracy.

The internet of things is a powerful combination of wireless devices, radio-frequency identification, and numerous sensors that offer the challenging but powerful potential of shaping current systems to make them smarter. IoT has many different areas of application and one such area is healthcare. The health sector is another promising area for IoT, and several businesses are exploring its application in this sector. In this study, it is proposed to collect healthcare data through the IoT and store it in the Interplanetary File System using Ethereum-based blockchain technology for data security. Blockchain technology is used in IoT to ensure the safety of collected data. Blockchain technology is used in IoT to ensure the safety of collected data. Smart contracts are used to automatically execute, control, and record events following the terms contained in them, enhancing the characteristics already present in the blockchain. IoT-based healthcare solutions are tested on many blockchain networks.

Reliability of Internet of Things seeks out beneficial problems by presenting, resolving, and certifying them. Preparations are essential in addition to offering IoT responsiveness for the huge preparation of advances in the Internet of Things in all domains of society. The purpose of this study is to propose, examine, and explore surveys in planned works and their future rights based on the results of the IoT in this particular case. IoT's fundamental critical components are the usage of repeating devices that maximise the benefits of mobile phones, sensors, as well as actuators. Networking, along with other formula-based assessments, has amplified node-to-node connectivity at different levels in terms of IoT reliability. In the present study work, dependability metrics and models have undergone a rigorous assessment. Evaluation of work analysis and future prospective of different research articles in the detailed view of the reliability of the Internet of Things. A Novel model for reliability technique is being proposed, which qualifies the accuracy, and precision using various machine learning algorithms can be used. The measurement of reliability in the IoT provides several study avenues as a result of this thorough investigation. Despite the sensitive nature of the research field, studies that access models of IoT dependability are now communicating widely.