A review of artificial intelligence techniques for selection &amp; evaluation

Ahmad, Ijaz; Liu, Yan; Javeed, Danish; Shamshad, Nadia; Sarwr, Danish; Ahmad, Shahab

doi:10.1088/1757-899x/853/1/012055

Cited by 16 publications

(9 citation statements)

References 33 publications

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“…To enable people of various technical backgrounds to utilise the power of IoT to improve their homes, we place a priority on ease of use. Additionally, [6] security and privacy are highly prioritised in our project. We use strong authentication and encryption technologies to protect the data communicated and stored within the smart home ecosystem, guaranteeing that sensitive data is kept private and shielded from online dangers.…”

Section: Introductionmentioning

confidence: 99%

Smart Home Automation using IoT: Prototyping and Integration of Home Devices

Singh,

Dhablia

2023

RJCSE

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Smart homes are becoming a reality thanks to the Internet of Things (IoT) technology's quick progress, providing homeowners with never-before-seen levels of efficiency, convenience, and security. This abstract gives a summary of a project that combines prototyping and integration to turn a standard house into a modern smart home. The paper focuses on creating an extensive IoT ecosystem that seamlessly connects diverse household equipment, such as lighting controls, thermostats, and security cameras, to a single, intelligent network. We use cutting-edge IoT communication and protocol technologies to create strong connectivity between these devices, enabling homeowners to remotely monitor and control them from a central hub or their smartphones. Our smart home system's central controller, which includes cutting-edge sensors and AI algorithms, is its brain. To reduce energy usage, improve security, and accommodate resident preferences and routines, this controller gathers and examines data from the linked devices. It learns user patterns through machine learning and suggests automation procedures to make life easier. A smart home system prioritises security and privacy in addition to increasing convenience. We also place a strong emphasis on flexibility and scalability, which enable the incorporation of new hardware and features as the IoT landscape changes. For homeowners, tech enthusiasts, and developers interested in building their own smart houses, our project on prototyping and integration serves as a model. It highlights how IoT technology has the ability to upgrade conventional living quarters into intelligent, networked areas that improve our quality of life while fostering energy efficiency and security. In the end, this project advances the continued development of smart home automation by increasing everyone's access to and ability to customise it.

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Section: Introductionmentioning

confidence: 99%

Smart Home Automation using IoT: Prototyping and Integration of Home Devices

Singh,

Dhablia

2023

RJCSE

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“…Since malignant tumors need early treatment because it is cancerous cell and spread abruptly. To limit and avoid future issues from occurring, the problem is a binary classification task to recognize malignant and benign issues that can be addressed using various machine learning and deep learning (ML/DL) algorithms [9][10][11][12][13][14][15][16][17][18]. e use of machine learning approaches to decrease the risk of developing cancer, recurrence, and survival prediction might increase the accuracy by 20% to 25% than last year [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection

Ahmad

Ullah

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body’s normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model’s efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

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“…The machine learning models used address the above informative issues. ML/DL model play an important implemented different dataset such as biomedical [7][8][9][10], agriculture [11,12] and specially IoT dataset [13].…”

Section: Introductionmentioning

confidence: 99%

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

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Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

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A review of artificial intelligence techniques for selection & evaluation

Cited by 16 publications

References 33 publications

Smart Home Automation using IoT: Prototyping and Integration of Home Devices

Smart Home Automation using IoT: Prototyping and Integration of Home Devices

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

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