The Smart Cradle System Basis on Internet of Things

Batool, None Aliya; HASHMI, BAQIR; ALI, AQIB; NAEEM, SAMREEN; BUKHARI, MUZAMIL; Khan, Muhammad Shoaib

doi:10.3390/mol2net-08-13940

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…SYNERGY-AI, an open, decentralized registry that can accelerate these efforts globally and at scale to cancer patients and access clinical trials using AI, has been developed since then. At cancer centers authorized by the National Cancer Institute (NCI) in the United States, this Massive Bio AI system is currently implemented [37]. Terms Definitions Area under curve A measure of the accuracy of a classifier when applied to a binary classification.…”

Section: Utilizing Ai To Get Optimal Therapymentioning

confidence: 99%

Current State of Artificial Intelligence (AI) in Oncology: A Review

Ali

NAEEM²,

Anam

et al. 2023

CTO

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Artificial intelligence (AI) has made significant strides in recent years toward resolving a wide range of biological issues, including a number of occurrences connected to cancer. Deep learning is an adaptable sector of AI that enables the automatic extraction of features. It is increasingly being used in various fields of cancer research, both scientific and clinical. In this study, several current applications of AI in oncology, including the situations in which deep learning has effectively addressed intractable disputes, are discussed. This study also discusses the challenges that need to be surmounted before such an application of AI may be implemented more broadly. Similarly, it brings to light resources and data sets that can assist in maximizing the potential of AI. Essential insights in oncology may be generated based on the development of novel methodologies and uses of AI, making it possible for substantial changes to be made to clinical practice.

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Section: Utilizing Ai To Get Optimal Therapymentioning

confidence: 99%

Current State of Artificial Intelligence (AI) in Oncology: A Review

Ali

NAEEM²,

Anam

et al. 2023

CTO

View full text Add to dashboard Cite

show abstract

“…Table 1 shows that researchers employed ML algorithms and approaches to detect attacks and anomalies with high accuracy. In investigations [70][71][72][73][74][75][76][77][78][79][80][81], researchers utilized and compared different ML algorithms. The Random Forest (RF) method obtained the best results with 99.34 %, 99.5 %, 99.9 %, 99.59 %, and 99.9 % accuracy.…”

Section: Comparatively Analysis Of ML Algorithms Used For Idsmentioning

confidence: 99%

Machine Learning Methods of IoT Security and Future Application

ALI

NAEEM

Anam

et al. 2022

PPASA

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One of the technologies that are now expanding rapidly is called the Internet of Things (IoT). It is a technology that enables billions of smart devices or things, collectively referred to as “Things,” to collect a variety of data about themselves and the environment in which they are located using a variety of sensors. They can then share data with parties who have been permitted to do so for a variety of objectives, such as the management and monitoring of industrial services or the expansion of company services or operations. However, there are presently more security risks associated with the Internet of Things than ever. The field of machine learning (ML) has recently experienced significant advancement in technology, which has resulted in the opening of various new lines of inquiry that may be used to address existing and upcoming issues related to the Internet of Things. Nevertheless, machine learning is a robust technology that can recognize suspicious dangers and activities in smart devices and grids. The authors of this paper conducted an extensive literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. After that, they compared several different ML algorithms regarding the detection of attacks and anomalies. Additionally, many machines learning-based Internet of Things protection systems have been presented.

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Section: Utilizing Ai To Get Optimal Therapymentioning

confidence: 99%

Genetic identification and phylogenetic relationships of fish species at Panjnad , Abbasia and Abu Dahbi canals (Punjab, Pakistan) based on mitochondrial COI sequences

Naz

Chatha²,

DANABAS3³

et al. 2022

CTO

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Fish is an extremely nutritious food that is abundant in important minerals, vitamin D, protein, and omega-3 fatty acids. Now a day, mislabeling of fish products is a major problem in the fish industry that affects the market prices and causes fluctuations. Phylogenetic analysis of nucleotide sequences is a technique that might be used to verify the identity of misidentified fish species. In this study five species (Cirrhinus mrigala, Hypophthalmichthys molitrix, two Actinopterygii sp., and Mastacembelus armatus) were used for phylogenetic analysis using the COI gene was amplified using the PCR, average product size is 658 base pairs. A comparison of obtained sequences was implemented through National Center for Biotechnology Information database (NCBI) and the Basic Local Alignment Search Tool (BLAST). Most resemble sequences were downloaded for comparison with our sequencing results. By using MAGA X the rates of different transitional substitutions are 11.33 and transversional substitutions are 6.83. The estimated Transition/Transversion bias (R) is 0.83. The K2P model was used for the estimation of this substitution pattern. Because most freshwater fish species were in the same order or family, phylogenetic tree analysis indicated that they grouped together. Using the InterPro database, the activity and regulatory pathways of the Cytochrome C Oxidase 1 gene are identified to show their activity on the protein level, especially for some important physiological functions. Each species of fish could be identified based on their speciation and node position and distance from their parent nodes. Based on the closest phylogenetic relations, DNA sequence-based study was developed for the accurate identification of fish species.

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The Smart Cradle System Basis on Internet of Things

Cited by 4 publications

References 9 publications

Current State of Artificial Intelligence (AI) in Oncology: A Review

Current State of Artificial Intelligence (AI) in Oncology: A Review

Machine Learning Methods of IoT Security and Future Application

Genetic identification and phylogenetic relationships of fish species at Panjnad , Abbasia and Abu Dahbi canals (Punjab, Pakistan) based on mitochondrial COI sequences

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