Madhuri Agrawal^*, Shikha Agrawal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493 - 15 March 2023

Abstract

Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the

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Yifeng Liu¹, Yangyang Li^1,*, Zhongxiong Xu¹, Xiaohan Liu¹, Haiyong Xie², Huacheng Zeng³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2519-2528, 2023, DOI:10.32604/iasc.2023.033286 - 15 March 2023

Abstract Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one of the crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during training. It effectively improves the performance of deep networks but ignores the importance of the differences between neurons. To optimize this issue, this paper presents a new dropout method called guided dropout, which selects the neurons to switch off according to the differences between the convolution kernel and preserves the informative neurons. It uses an unsupervised clustering algorithm… More >

Ali Alqahtani¹, Abdulaziz A. Alsulami^2,*, Saeed Alahmari³, Mesfer Alrizq⁴

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2505-2518, 2023, DOI:10.32604/iasc.2023.033243 - 15 March 2023

Abstract The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise… More >

Ragd Alhejaily¹, Rahaf Alhejaily¹, Mai Almdahrsh¹, Shareefah Alessa¹, Saleh Albelwi^1,2,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2669-2684, 2023, DOI:10.32604/iasc.2023.033062 - 15 March 2023

Abstract Football is one of the most-watched sports, but analyzing players’ performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player’s performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to… More >

Adel Alshamrani^1,*, Abdullah Alshahrani²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835 - 15 March 2023

Abstract The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which More >

P. S. Arthy^1,*, A. Kavitha²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511 - 15 March 2023

Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge.… More >

M. Hari Prabhu^*, K. Sundararaju

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2947-2958, 2023, DOI:10.32604/iasc.2023.032328 - 15 March 2023

Abstract The power system is facing numerous issues when the distributed generation is added to the existing system. The existing power system has not been planned with flawless power quality control. These restrictions in the power transmission generation system are compensated by the use of devices such as the Static Synchronous Compensator (STATCOM), the Unified Power Quality Conditioner (UPQC) series/shunt compensators, etc. In this work, UPQC’s plan with the joint activity of photovoltaic (PV) exhibits is proposed. The proposed system is made out of series and shunt regulators and PV. A boost converter connects the DC… More >

V. Prakash^*, S. Gopalakrishnan

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122 - 15 March 2023

Abstract Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software… More >

K. Naresh Kumar^1,*, S. Srinath²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2623-2638, 2023, DOI:10.32604/iasc.2023.031713 - 15 March 2023

Abstract This paper concentrates on compensating the power quality issues which have been increased in day-to-day life due to the enormous usage of loads with power electronic control. One such solution is compensating devices like Pension Protection Fund (PPF), Active power filter (APF), hybrid power filter (HPF), etc., which are used to overcome Power Quality (PQ) issues. The proposed method used here is an active compensator called unified power quality conditioner (UPQC) which is a combination of shunt and series type active filter connected via a common DC link. The primary objective is to investigate the… More >

Mercy Sharon Devadas¹, N. Bhalaji^1,*, Xiao-Zhi Gao²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2685-2702, 2023, DOI:10.32604/iasc.2023.031636 - 15 March 2023

Abstract In Mobile ad hoc Networks (MANETs), the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss. In this paper, a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling (MFPA-FSPS) Mechanism is proposed for sustaining Quality of Service (QoS) in the network. This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’ priority for guaranteeing optimal network performance. It further used the modified Firefly More >