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The Internet of Things becomes Internet of Everything when in the process of communication machine-to-machine also intelligent forms of communication between human and machine are involved. Cities can be viewed as a microcosm of this interconnected system where ICT and emerging technologies can be enabling factors to transform cities in Smart Citie...
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... 3: Execution of the experiment Given as input the Nace rev 2 document, the execution of the data quality control with the LanguageTool library amended in the step 2 of the experiment, has detected a series of errors of different types present in the document; that is, tabulation, typing and syntactic errors. Tabulation errors are those relating to missing or exceeding spaces and missing or exceeding punctuation, such as those highlighted in Figure 2. ...
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... AI can be used to warn the public or local law enforcement officials of potentially upsetting circumstances [9][10][11] . Drones are widely used and applied for military and defense purposes 12,13 . Drones come in various sizes, from military drones 200 feet to small flying machines. ...
The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00–91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.
... This system gives up to 90% accuracy and helps the user to get the response in shorter time and with appropriate result, but the collected data is not well analyzed and only limited to the current student of the college and does not serve purpose for prospective candidate of the college. Barletta et al. (2019) created a software tool which will be used by any college to help the students to freely upload their queries. It uses the artificial intelligence algorithm to fetch the suitable answers for the user queries, the chatbot uses AIML as background to knowledge for processing the response, The system fetches information from the AIML file database, finally it produced a software tool used by college to help the students to freely upload their database, however the system does not make use of a standard database management system. ...
The development of the chatbot system is an algorithm that analyzes the student queries and replies to messages. In this system, artificial intelligence is built to answer the query of the student. The specific objectives are to determine the required features for the construction of the knowledge base, design and implement the model, and evaluate the performance of the developed system. Samples of Frequently Asked Questions (FAQ) were collected from the Department of Student Affairs, Admission Office, and Information Management and Technology Center (IMTC) of the university. The collected sample was analyzed based on the category of question and the model was designed using Unified Modeling Language (UML). The model was implemented with a python programming language, HTML, CSS, and JavaScript for the client server side, and also Artificial Intelligence Markup Language (AIML) () and MySQL for the back end. The developed system performance was evaluated using Alpha Beta testing. The proposed system was successfully tested to denote its effectiveness and achievability. It totally eliminates the manual process of retrieving information about a particular domain and reduces manpower, and time, for any individual. The developed system will provide adequate assistance to the student on FAQ, thereby reducing the time in visiting the college to enquire about the information in respect of school activities. It will also provide an enabling environment for the students to keep updated about the school activities.
... This system gives up to 90% accuracy and helps the user to get the response in shorter time and with appropriate result, but the collected data is not well analyzed and only limited to the current student of the college and does not serve purpose for prospective candidate of the college. Barletta et al. (2019) created a software tool which will be used by any college to help the students to freely upload their queries. It uses the artificial intelligence algorithm to fetch the suitable answers for the user queries, the chatbot uses AIML as background to knowledge for processing the response, The system fetches information from the AIML file database, finally it produced a software tool used by college to help the students to freely upload their database, however the system does not make use of a standard database management system. ...
The development of chatbot system is an algorithm that analyzes the student queries and reply messages. In this system, artificial intelligence is built to answer the query of the student. The specific objectives are to determine the required features for the construction of knowledge base, design and implement the model, evaluate the performance of the developed system. Samples of Frequently Asked Questions (FAQ) was collected from the department of Student Affairs, Admission Office and Information Management and Technology Center (IMTC) of the university. The collected sample was analyzed based on the category of question and the model was designed using Unified Modeling Language (UML). The model was implemented with python programming language, HTML, CSS, JavaScript for the client sever side, and also Artificial Intelligence Markup Language (AIML) () and MySQL for the back end. The developed system performance was evaluated using Alpha Beta testing. The proposed system was successfully tested to denote its effectiveness and achievability. It totally eliminates the manual process of retrieving information about a particular domain and reduces manpower, time, for any individual. The developed system will provide adequate assistance to the student on FAQ, thereby reducing the time in visiting the college to enquire about the information in respect of school activities. It will also provide an enabling environment for the students to keep them updated about the school activities.
... Today, both public and private entities appreciate the value of data because this resource has already proved to be the key to improving efficiency and effectiveness in everyday activities [31]. For the SC, the number of stakeholders is higher and more diverse than in the private business case, i.e., utility companies, transport providers, mobile phone operators, social media sites, financial institutions, surveillance and security providers, emergency services, and others, along with the citizens themselves [28]. ...
... However, there are situations in which there is the need to perform data extraction from available data sources using different techniques. What is important for any application is the quality of gathered data, in order to have a correct representation of the real world and to be fitted for their intended use [31]. Furthermore, the following aspects are also extremely relevant for developing applications for smart cities [39]: (1) storing and managing databases, as large amount of data is collected, and (2) integrating data from many sources. ...
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.
... The word matching process used to decide the level of similarity of words to enable the percentage of similarity known. Ratcliff/Obershelp algorithm used for string matching by finding the longest substring of two strings, s1 and s2, if they have the same longest string similarity called anchor, then the anchor's right and left strings will be process as if they are new strings, this process repeated until all characters of the two strings analyzed [6]. In a study the algorithm used to normalize microtexts into English to improve the accuracy of sentiment analysis classification [7] at the experimental stage using 4,000 random tweets and Similarity Evaluation using Ratcliff/Obershelp patternmatching algorithm. ...
The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ UNSW-NB15, CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD. The system attains precision values of 89.10% and 90.16%, accuracy rates of 91.00% and 91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.
In this digital era, the City Government Bandung has 394 Smart City applications built since 2014. All
the applications were built with the aim to facilitate the work of all Regional Apparatus Organization as
well as facilitate public services. All applications were developed by a team from the Office of
Communications and Informatics of Bandung city in cooperation with programmers and
communication experts from universities and professionals. The built application is intended to
facilitate services to the public. Logically this is important considering that many people in Bandung
City who have a smartphone wherein the play store there are 1000 applications, while we only install
as many as 394 Smart City apps mean still many that have not been utilized. Of the many
applications built by Bandung City Government, some applications are replicated by dozens of
municipal district governments throughout Indonesia. One application that is replicated is
Performance Accountability System Government Agencies (PASGA) used by the City Government of
Tangerang and the Paya Kumbuh Government. So far, local governments that have signed a
Cooperation Agreement (CA) with the office of Communication and Information district like Bandung,
there are 34 districts/cities throughout Indonesia. This paper aims to find out about a number of
descriptions of Bansund as a smart city, including: (1) How the position of communication strategy in
a smart city; (2) How far the opinion of Bandung City society about Smart City policy as New
Paradigm in Developing Country based on six indicators; (3) What is Smart City City Manager
Bandung's opinion about the demands of completion of Government tasks in Indonesia; and (4) How
far is the opinion of Bandung City society about Smart City Mobile Utilization in Indonesian Building as
Smart Country.
