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| 1 | Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors显示文摘Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits. | Kainat Ibrar Abdul Muiz Fayyaz Muhammad Attique Khan Majed Alhaisoni Usman Tariq Seob Jeon Yunyoung Nam | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 2 | Anomalous Situations Recognition in Surveillance Images Using Deep Learning显示文摘Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,etc.Deep learning models’learning capability is leveraged to identify abnormal situations with improved accuracy.This work proposes a deep learning architecture called Anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image situations.The proposed framework has five steps.In the first step,pretraining of the proposed architecture is performed on the CIFAR-100 dataset.In the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset.In the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth step.Finally,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous situations.The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000.The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM. | Qurat-ul-Ain Arshad Mudassar Raza Wazir Zada Khan Ayesha Siddiqa Abdul Muiz Muhammad Attique Khan Usman Tariq Taerang Kim Jae-Hyuk Cha | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 3 | Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check显示文摘An effective communication application necessitates the cancellation of Impulsive Noise(IN)from Orthogonal Frequency Division Multiplexing(OFDM),which is widely used for wireless applications due to its higher data rate and greater spectral efficiency.The OFDM system is typically corrupted by Impulsive Noise,which is an unwanted short-duration pulse with random amplitude and duration.Impulsive noise is created by humans and has non-Gaussian characteristics,causing problems in communication systems such as high capacity loss and poor error rate performance.Several techniques have been introduced in the literature to solve this type of problem,but they still have many issues that affect the performance of the presented methods.As a result,developing a new hybridization-based method is critical for accurate method performance.In this paper,we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM.The proposed method is also compared to Least Mean Square(LMS),Normalized Least Mean Square(NLMS),and Recursive Least Square(RLS)adaptive filters.It has also been tested using the binary phase-shift keyed(BPSK),four quadrature amplitude modulation(QAM),sixteen QAM,and thirty-two QAM modulation techniques.Bit error Rate(BER)simulations are used to evaluate system performance,and improved performance is obtained.Furthermore,the proposed method is more effective than recent methods. | Attia Irum Abdul Muiz Fayyaz Sara Ayub Mudassar Raza Majed Alhaisoni Muhammad Attique Khan Abdullah Alqahtani Heebum Kim Byeong-Gwon Kang | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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