Husna Jamal Abdul Nasir
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Preferred name
Husna Jamal Abdul Nasir
Official Name
Jamal Abdul Nasir, Husna
Alternative Name
Abdul Nasir, Husna Jamal
Nasir, Husna Jamal Abdul
Main Affiliation
Scopus Author ID
36160497300
Researcher ID
ISU-2147-2023

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Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture

2024 , Mohd Mizan Munif , Husna Jamal Abdul Nasir , Muhammad Imran Ahmad

<span>Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.</span>

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Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture

2024-01-01 , Munif M.M. , Husna Jamal Abdul Nasir , Muhammad Imran Ahmad

Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.

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Hybrid packet routing algorithm based on ant colony system and tabu search in wireless sensor network

2023-11-01 , Husna Jamal Abdul Nasir , Ku Mahamud K.R. , Kamioka E.

Packet routing in wireless sensor network is one of the most crucial aspects as it controls the way packets move through sensor nodes with various capabilities to reach to the destination node. Inefficient routing process may lead to higher energy consumption, higher failure rate, and lower throughput. Metaheuristic algorithms have been some of the common approaches to solve these problems due to their adaptability with dynamic environment. This paper proposed a hybrid metaheuristics routing algorithm that hybridizes ant colony system and tabu search which focuses on exploitation and exploration mechanism while reducing the local optima. The proposed algorithm uses ant colony system technique to discover the best path for packet transmission by considering the energy level of each sensor node. Additionally, tabu search technique is applied to overcome the local optima problem by temporarily suspending the bad nodes and initiate backward movement with the aim to prevent the search agent from getting trapped in a blind alley. The proposed hybrid routing algorithm was evaluated against single and hybrid routing algorithms in terms of throughput, energy consumption, and energy efficiency. Experimental results showed that the proposed algorithm outperformed the other routing algorithms in terms of throughput, energy consumption, and energy efficiency.

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Energy efficient ant colony system for packet routing in Wireless Sensor Network

2022-01-01 , Husna Jamal Abdul Nasir , Ku-Mahamud K.R. , Kamioka E.

Routing packets in Wireless Sensor Network (WSN) is challenging due to the distribution of sensor nodes with different ability. Inefficient routing may lead to higher failure rate, higher latency and higher energy consumption. One of the common approaches to solve this problem is by using bio-inspired routing algorithms due to their abilities to adapt with dynamic environment. This paper proposed an improved ant colony system for packing routing in WSN that focuses on exploration and exploitation techniques. In the proposed routing algorithm, the best path to be used for packet transmission will be determined by considering the remaining energy of each sensor node to reduce the hotspot problem. Local pheromone update and global pheromone update are used with the aim to prevent imbalanced energy depletion of sensor nodes and to balance the packet distribution. The proposed routing algorithm was validated against several bio-inspired routing algorithms in medium and large sized networks. The results suggested that it has outperformed in terms of success rate, packet loss rate, latency and energy efficiency.

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