Osman Ali - Academia.edu (original) (raw)

Papers by Osman Ali

Emotion is an interdisciplinary research field investigated by many research areas such as psycho... more Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human-robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work. Keywords Emotion recognition analysis Á Physical signals Á Intrusive and non-intrusive emotion recognition Á Physiological signals Á Facial expressions Á Speech stimuli Á Body postures and gestures Á Machine learning and deep learning techniques

At numerous phases of the computational process, pattern matching is essential. It enables users ... more At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method.

Pattern matching is a highly useful procedure in several stages of the computational pipelines. F... more Pattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like complexity, efficiency, and techniques. Which algorithm is best for which DNA sequence and why? This describes the different algorithms for various activities that include pattern matching as an important aspect of functionality. This article shows that BM, Horspool, ZT, QS, FS, Smith, and SSABS methods employ the bad character preprocessing function. In addition, BM, SSABS, TVSBS, and BRFS methods are using two approaches in the preprocessing stage, which decreases the preprocessing time. Furthermore, KR, QS, SSABS, BRFS, and Shift-Or are not recommended for the long pattern, whereas ZT, FS, d-BM, Raita, and Smith are not recommended for the short pattern. This is because they are time-consuming and certain algorithms, such as ZT and DCPM, use a lot of time and space during the matching and search process, while others, such as d-BM and TSW, save space and time. Although DCPM, BRFS, and QS are quicker than other algorithms, FLPM, PAPM, and LFPM rank highest in terms of complexity time.

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobeh... more Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful

IBIMA 2005, 2005

Indexing a document is the method for describing its content for sake of easier subsequent retrie... more Indexing a document is the method for describing its content for sake of easier subsequent retrieval in a document storage. This paper describes the implementation of the automatic indexing of various term weighting schemes in an IR (Information Retrieval) system using CISI documents collection which constitutes of abstracts for information retrieval papers and NPL collection which constitutes of abstracts for electronic engineering documents. The system starts with a simple form of text representation in which extracts keywords that represent documents as vectors of weights that represent the importance of keywords in documents of the documents collection and then evaluates, compares the retrieval effectiveness of various search models based on automatic text-word indexing and presents experimental results conduct to study the improvements made on the effectiveness of the text retrieval by successively applying these approaches.

IBIMA 2005, 2005