Low-Cost Gas Leak Detection and Surveillance System for Single Family Homes Using Wit.ai, Raspberry Pi and Arduino

Pérez-Palomino, Ximena; Rosas-Paredes, Karina; Tejada, Jose Esquicha

doi:10.3991/ijim.v16i09.30177

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…For the first research question "What kind of technologies are used in chatbot devel-opment? ", the data of interest are: programming languages for chatbot development like Java, Clojure, Python, C++, PHP, Ruby, etc; Natural language understanding cloud platforms like DialogFlow [23], wit.ai [24], Microsoft's language understanding (LUIS) [25], Conversation AI [26], etc; and Chatbot development technologies like Rasa [27], Botsify [28], Chatfuel [29], Manychat [30], etc.…”

Section: Data Collectionmentioning

confidence: 99%

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Omarov¹,

Narynov²,

Zhumanov³

2023

Computers, Materials &Amp; Continua

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Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from "virtual psychiatrists" to "social robots" in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.

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Section: Data Collectionmentioning

confidence: 99%

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Omarov¹,

Narynov²,

Zhumanov³

2023

Computers, Materials &Amp; Continua

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“…The UDP flooding assault [20] is a type of data syn flood assault in which the chosen target is overrun by a constant stream of data circulation at a higher bit rate and packet scope than normal. IDS are used to monitor and identify network infractions to recognize and respond to them [21]. As a result, it is critical to successfully implement and manage such systems to assure the integrity and availability of network services [19] [22].…”

Section: Introductionmentioning

confidence: 99%

An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory

Hanafi,

Saheed,

Arowolo

2023

Int. J. Interact. Mob. Technol.

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A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs; however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for DBN and LSTM accuracy reaching 99.75%, a sensitivity of 99.79%, and a recall of 99.79%.

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