Bøker av Prathibhavani P. M.

É incrível como o mundo inteiro está totalmente dependente dos dados. A geração de dados é a forma como os humanos falam e também é verdade que consomem dados através de todos os meios possíveis. Com as actualizações e actualizações frequentes e regulares na tecnologia, os seres humanos têm testemunhado o crescimento de dispositivos inteligentes ao contrário de qualquer outro. Também assistimos a um aumento significativo na utilização da Internet das Coisas (IoT) e, para alguns de nós, esta tem sido parte integrante da vida quotidiana. Assim, não ficaria surpreendido quando nos apercebemos de que os dispositivos da Internet das Coisas (IdC) que estamos a utilizar são vulneráveis a vários ataques maliciosos. Estes dispositivos IoT são elementares tanto na concepção como na segurança. A sua fragilidade intrínseca de concepção é explorada durante os ataques de firmware e a sua segurança de rede de baixo nível é facilmente penetrada durante os ciberataques. Um desses ciberataques comuns é a Negação Distribuída de Serviços (DDoS) e o trabalho proposto visa abordar esta questão contra uma rede de dispositivos IoT.

È incredibile come il mondo intero sia totalmente dipendente dai dati. La generazione di dati è il modo in cui gli esseri umani parlano ed è anche vero che consumano dati attraverso ogni mezzo possibile. Con i frequenti e regolari aggiornamenti e upgrade della tecnologia, gli esseri umani hanno assistito alla crescita di dispositivi intelligenti come nessun altro. Abbiamo anche assistito a un aumento significativo dell'utilizzo dell'Internet degli oggetti (IoT) e per alcuni di noi è stato parte integrante della vita quotidiana. Di conseguenza, non ci stupiremmo se ci rendessimo conto che i dispositivi IoT che utilizziamo sono vulnerabili a vari attacchi dannosi. Questi dispositivi IoT sono elementari sia nella progettazione che nella sicurezza. La loro intrinseca fragilità progettuale viene sfruttata durante gli attacchi al firmware e la loro sicurezza di rete di basso livello viene facilmente penetrata durante gli attacchi informatici. Uno di questi attacchi informatici comuni è il DDoS (Distributed Denial of Services) e il lavoro proposto mira ad affrontare questo problema contro una rete di dispositivi IoT.

Es ist unglaublich, wie sehr die gesamte Welt von Daten abhängig ist. Datengenerierung ist die Sprache der Menschen, und es ist auch wahr, dass sie Daten über jedes mögliche Medium konsumieren. Mit den häufigen und regelmäßigen Aktualisierungen und Upgrades in der Technologie haben die Menschen das Wachstum von intelligenten Geräten erlebt, die ihresgleichen suchen. Auch die Nutzung des Internets der Dinge (Internet of Things, IoT) hat deutlich zugenommen und ist für einige von uns zu einem festen Bestandteil des täglichen Lebens geworden. Daher wäre es nicht verwunderlich, wenn wir feststellen, dass die von uns verwendeten IoT-Geräte anfällig für verschiedene bösartige Angriffe sind. Diese IoT-Geräte sind sowohl im Design als auch in der Sicherheit elementar. Ihre inhärente Designschwäche wird bei Firmware-Angriffen ausgenutzt, und ihre niedrige Netzwerksicherheit kann bei Cyberangriffen leicht durchbrochen werden. Eine solche häufige Cyber-Attacke ist Distributed Denial of Services (DDoS), und die vorgeschlagene Arbeit zielt darauf ab, dies gegen ein Netzwerk von IoT-Geräten anzugehen.

Il est incroyable de constater que le monde entier est totalement dépendant des données. La génération de données est la façon dont les humains parlent et il est également vrai qu'ils consomment des données par tous les moyens possibles. Avec les mises à jour et les mises à niveau fréquentes et régulières de la technologie, les êtres humains ont assisté à la croissance d'appareils intelligents comme aucun autre. Nous avons également constaté une augmentation significative de l'utilisation de l'Internet des objets (IoT) et pour certains d'entre nous, il fait partie intégrante de la vie quotidienne. Il ne serait donc pas surprenant que nous réalisions que les dispositifs IoT que nous utilisons sont vulnérables à diverses attaques malveillantes. Ces dispositifs IoT sont élémentaires tant au niveau de leur conception que de leur sécurité. Leur fragilité de conception intrinsèque est exploitée lors d'attaques de micrologiciels et leur sécurité réseau de bas niveau est facilement pénétrée lors des cyber-attaques. L'une de ces cyber-attaques courantes est le déni de service distribué (DDoS) et le travail proposé vise à y remédier contre un réseau de dispositifs IoT.

Climate change has had a negative impact on the performance of most crops in India over the previous two decades. Crop yield prediction ahead of time would assist farmers and policymakers in determining appropriate marketing, transportation, and storage strategies. This proposed method will assist farmers in determining the crop yield prior to the cultivation of the agricultural land, allowing them to make informed decisions. In this work, first identify the factors that influence crop output to effectively predict the yield. Temperature, soil moisture, humidity, solar radiation, and pH value are all important factors. Needed to collect and analyze data on these factors for our benefit and there are various ways or algorithms for such data analysis in crop prediction, and can predict the crop yield with the help of these algorithms. In this proposed method, would like to look at the problem from the perspective of Machine Learning by evaluating various algorithms such as Random Forest, Simple Linear Regression (SLR), and Neural Networks to guarantee that considered the best algorithm and achieve the highest possible accuracy.

It is incredible how the entire world is totally dependent on the data. Data generation as how human speak and it is also true that they consume data through every possible medium. With the frequent and regular updates and upgrades in technology, human beings have witnessed the growth of smart devices unlike any. We have also seen a significant increase in the usage of the Internet of Things (IoT) and for some of us, it has been an integral part of daily life. Hence, it wouldn't be surprised when we realize that the IoT devices we are using are vulnerable to various malicious attacks. These IoT devices are elemental in both design and security. Their intrinsic design frailty is exploited during firmware attacks and their low-level network security is easily penetrated during the cyber-attacks. One such common cyber-attack is Distributed Denial of Services (DDoS) and the proposed work aims to address this against a network of IoT devices.

Convolutional Neural Networks (CNN) and Machine Learning (ML) have evolved a long way in the modern technological era. They are used in analysis and prediction of various segments of normal life. They have a greater advantage when it comes to understanding a process, sometimes even better than a human brain. The Virus that is generated as COVID-19 is mainly caused when a person coughs, sneeze or exhales and it is transmitted as a small particle to other bodies and form these viruses. One of the major tricks for stopping the spread of this covid-19 is social distancing and rapid testing. This rapid test will take 2-3 days of time to get the result of covid-19. This may trouble a large amount of people in terms of money and time. Therefore, with the advantage of CNN, devised a method to increase the efficiency of the testing process. X-rays and CT scans show considerable advantage in detecting COVID-19 in a person. COVID-19 affects the lungs primarily which could be caught in X-rays as a white overlay. Constructed a model that could identify whether the submitted X-ray is of a normal person or of a COVID-19 positive person.