Search Results - (( intelligence based growth algorithm ) OR ( intelligence based m algorithm ))

An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification by Hussain, A., Hussain, S.S., Uddin, M.M., Zubair, M., Kumar, P., Umair, M.

“…During the past decade, an intensive growth of Humanâ��Computer Interaction (HCI) has been evolved. …”
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Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

“…The growth of intelligent intrusion and diverse attack techniques in network systems stimulate computer scientists and mathematical researchers to challenge the dangers of intelligent attacks. …”
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Intelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environment by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S.

“…In this paper, the optimised Apriori association rule algorithm is used as technical support to realise the function of interlocking fault diagnosis in the intelligent fault diagnosis system module. …”

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

“…In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. …”
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iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems by Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur

“…For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. …”
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Recent advances of whale optimization algorithm, its versions and applications by Alyasseri Z.A.A., Ali N.S., Al-Betar M.A., Makhadmeh S.N., Jamil N., Awadallah M.A., Braik M., Mirjalili S.

“…One of the SI algorithms is called the whale optimization algorithm (WOA). …”

A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier by Azad, Saiful, Amin Salem, Saleh Bllagdham, Mahmud, Mufti, Kaiser, M. Shamim, Miah, Md Saef Ullah

“…Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern Growth (iFP-Growth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. …”
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A medical cyber-physical system utilizing the bayes algorithm for post-diagnosis patient supervision by Mizanur, Rahman, Talha, Sarwar, Ahmed, Zahiduddin, Miah, M. Saef Ullah, Bhowmik, Abhijit, Nusrat, Fahmeda, Junaida, Sulaiman

“…On the other hand, these problems can be solved by intelligent and adaptive systems, which is now possible thanks to the growth of relevant technology, especially healthcare 4.0. …”
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User-centric learning for multiple access selections by Dzulkifly S., Hashim W., Ismail A.F., Dohler M.

Absorption Coefficient of Bulk III-V Semiconductor Materials: A Review on Methods, Properties and Future Prospects by Lee H.J., Gamel M.M.A., Ker P.J., Jamaludin M.Z., Wong Y.H., David J.P.R.

Stereo Camera-Based Aerial Target Geolocation in a Ground-Based Platform Environment by Amir Hilmi Bin Ahmad Azizi

Haemoglobin distribution in ulcers for healing assessment by Malik, Aamir Saeed, Fadzil, A. F. M., Arshad, Leena, Jamil, Adawiyah, Yap, Felix

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Multi-Objective Optimisation of CNC Milling Process for Al 6061 using Modified NSGA-II by M. F. F., Ab Rashid, N. M. Zuki, N. M., A. N. M., Rose

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Towards Autonomous Farming -A Novel Scheme based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…To this end, several models are proposed in the literature that are based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. …”
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Towards Autonomous Farming -A Novel Scheme based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…To this end, several models are proposed in the literature that are based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. …”
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Toward Autonomous Farming - A Novel Scheme Based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control by Ullah, I., Fayaz, M., Aman, M., Kim, D.

“…To this end, several models are proposed in the literature that is based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. …”
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An analysis of scheduling scheme for QoS guaranteed interactive multimedia over high speed wireless campus networks by Alam, M.K., Abd Latif, Suhaimi, Masud, Mosharrof Hussain, Akter, Mahmuda

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Disease detection of solanaceous crops using deep learning for robot vision by Ahmad Radzi, Syafeeza, A.Halim, Nurul Hidayah, Abd Razak, Norazlina, Mohd Saad, Wira Hidayat, Wong, Yan Chiew, Amsan, Azureen Naja

“…Other research works are focusing on improving the mean average precision and the best result reported so far is 93% of mean Average Precision (mAP) by YOLOv5. This paper focuses on object detection of the Convolutional Neural Network (CNN) architecture-based to detect the disease of solanaceous crops for robot vision. …”
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A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption by Chiroma, H., Abdullahi, U.A., Hashem, I.A.T., Saadi, Y., Al-Dabbagh, R.D., Ahmad, M.M., Dada, G.E., Danjuma, S., Maitama, J.Z., Abubakar, A., Abdulhamid, S.�M.

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Solving university examination timetabling problem using intelligent water drops algorithm by Aldeeb B.A., Norwawi N.M., Al-Betar M.A., Jali M.Z.B.