Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

University web portals are considered one of the main access gateways for universities. Accessibility of university online services is a major issue for undergraduate and graduate students with disabilities. Online registration makes people with disabilities more independent to register courses, add, drop courses, or attend courses independently. Yet, many people with disabilities in Palestine face major challenges when using university websites. In order to understand the issues that face people with disabilities when they use websites of Palestinian universities, this study evaluates the accessibility of the home pages of these universities during COVID-19. In order to evaluate partially the accessibility of Palestinian universities during COVID-19, we apply automatic evaluation on all homepages of eighteen Palestinian universities. The most violated guideline is empty link which is related to success criterion 2.4.4 Link Purpose. The second highest violated error is linked image missing alternative text which is related to the success criterion 1.1.1 Non-text Content. The obtained results show that all the universities websites are not conforming to Web Content Accessibility Guidelines (WCAG) 2.0 level A. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

Since the past decades, the world has been transformed into a digital society, where every individual is living with a unique identifier. The primary purpose of this id is to distinguish from others and to deal with digital machines which are surrounding the world. Recently, many researchers showed that the brain electrical activity or electroencephalogram (EEG) signals could provide robust and unique features that can be considered as a new biometric authentication technique, given that accurately methods to decompose the signals must also be considered. This paper proposes a novel method for EEG signal denoising based on the multi-objective Flower Pollination Algorithm and the Wavelet Transform (MOFPA-WT) to extract useful features from denoised signals. MOFPA-WT is tested using a standard EEG signal dataset, namely, EEG motor movement/imagery dataset, and its performance is evaluated using three criteria: (i) accuracy, (ii) true acceptance rate, and (iii) false acceptance rate. We show that the proposed method can achieve results that are comparable to the state-of-the-art ones, as well as we draw future directions towards the research area.

Summary Flash memory–based solid‐state drives (SSDs) offer several attractive features and benefits compared to hard disk drive (HDD), such as shock resistance and better performance especially for random data access. Depending on the number of bits in each cell, Flash memory can be designed as single/multi/triple level cell (SLC/MLC/TLC), which have different performance, density, cost and write endurance characteristics. To bring the best of these together, several researchers have proposed designing SSD using hybrid SLC/MLC/TLC Flash memory. However, these SSDs also present several challenges such as buffer management, placement of hot/cold data in suitable portion, and intelligent garbage collection. Several recent techniques aim to address these challenges. In this paper, we present a survey of techniques for managing SSDs designed with SLC/MLC/TLC Flash memory. We classify the works on several axes to bring out their similarities and differences. We aim to synthesize the state‐of‐art progress in hybrid SSD management and also spark further research in this area.

A new variant of the Genetic Algorithm (GA) inspired by monogamy mating system is put forward. The Monogamous Pairs Genetic Algorithm (MopGA) incorporates two important operations: pair bonding and infidelity at a small probability. With pair bonding, parents continue to mate at each iteration until their bond expires. In the meantime, infidelity generates variety and promotes diversity via mating with extrapair. We evaluate the algorithm's performance using various parametrizations and making comparisons to the Standard Genetic Algorithm (SGA) based on the Hierarchical If-and-Only-If (HIFF) and Deceptive (DP) functions. Empirical results show that incorporating pair bonding is a practical move. Improvement in performance in terms of solution quality and computational efforts have been observed for all test problems. Additionally, we also report the effectiveness of MopGA in handling easy and difficult sudoku puzzles.

The Asymmetric Traveling Salesman Problem (ATSP) is one of the Combinatorial Optimization Problems that has been intensively studied in computer science and operations research. Solving ATSP is NP-hard and it is harder if the problem is with large scale data. This paper intends to address the ATSP using an hybrid approach which integrates the generic Bee Colony Optimization (BCO) framework and an insertion-based local search procedure. The generic BCO framework computationally realizes the bee foraging behaviour in a typical bee colony where bees travel across different locations to discover new food sources and perform waggle dances to recruit more bees towards newly discovered food sources. Besides the bee foraging behaviour, the generic BCO framework is enriched with an initialization engine, a fragmented solution construction mechanism, a local search and a pruning strategy. When the proposed algorithm is tested on a set of 27 ATSP benchmark problem instances, 37% of the benchmark instances are constantly solved to optimum. 89% of the problem instances are optimally solved for at least once. On average, the proposed BCO algorithm is able to obtain 0.140% deviation from known optimum for all the 27 instances. In terms of the average computational time, the proposed algorithm requires 48.955s (< 1 minutes) to obtain the best tour length for each instance.

Accessibility of e-government services is a key issue for people with disabilities. E-government services can significantly save lot of their effort and provide them with lot of easy to reach services. Yet, accessibility of e-government websites is still under-explored topic in Jordan. In order to understand the accessibility of e-government websites and its problems, this study evaluates a set of e-government websites using 20 blind and visually impaired volunteers and at the same time conducts a survey on e-government websites developers. The results from e-government websites accessibility evaluation are compared with expert's review. For both the evaluation and the survey we used a set of accessibility guidelines developed by W3C [i.e. Web Content Accessibility Guidelines (WCAG 2.0)], Section 508 of the US Rehabilitation Act Amendments of 1998, and other literature review. In order to evaluate a reasonable number of e-government Web sites, a set of common e-government websites visited by the blind community were identified and a set of specific common tasks to test were defined. The analysis of the research results revealed a serious weakness in understanding, adopting and implementing Web accessibility guidelines throughout nearly all Jordanian e-government websites. Improving awareness, training developers and users, and developing formal guidelines of Web accessibility are needed to enable visually impaired and blind users in accessing e-government Web sites and their services. Further research analysis discusses and identifies key areas in which e-government accessibility can be enhanced.

University course timetabling problem (UCTP) is considered to be a hard combinatorial optimization problem to assign a set of events to a set of rooms and timeslots. Although several methods have been investigated, due to the nature of UCTP, memetic computing techniques have been more effective. A key feature of memetic computing is the hybridization of a population-based global search and the local improvement. Such hybridization is expected to strike a balance between exploration and exploitation of the search space. In this paper, a memetic computing technique that is designed for UCTP, called the hybrid harmony search algorithm (HHSA), is proposed. In HHSA, the harmony search algorithm (HSA), which is a metaheuristic population-based method, has been hybridized by: 1) hill climbing, to improve local exploitation; and 2) a global-best concept of particle swarm optimization to improve convergence. The results were compared against 27 other methods using the 11 datasets of Socha et al. comprising five small, five medium, and one large datasets. The proposed method achieved the optimal solution for the small dataset with comparable results for the medium datasets. Furthermore, in the most complex and large datasets, the proposed method achieved the best results.

In this paper, three selection mechanisms in memory consideration operator for Examination Timetabling Problem with Harmony Search Algorithm (HSA) are investigated: Random memory consideration which uses a random selection mechanism, global-best memory consideration which uses a selection mechanism inspired by a global best concept of Particle Swarm Optimisation (PSO), and Roulette-Wheel memory consideration which uses the survival for the fittest principle. The HSA with each proposed memory consideration operator is evaluated against a de facto dataset defined by Carter et al., (1996). The results suggest that the HSA with Roulette-Wheel memory consideration can produce good quality solutions. The Results are also compared with those obtained by 6 comparative methods that used Carter dataset demonstrating that the proposed method is able to obtain viable results with some best solutions for two testing datasets.

Evolutionary algorithm is one of the major classes of stochastic search methods. This algorithm searches the problem space by exploring and exploiting the search space. The balance between exploration and exploitation will change throughout the search process. Maintaining the right balance between the exploration and exploitation in the search process is crucial for the success of the search process. The parameter values of the algorithm play a crucial role in determining the nature of the search, whether explorative or exploitative. In this paper, we propose an adaptive parameter controlling approach using harmony search. During the search process, harmony search directs the search from the current state to a desired state by determining suitable parameter values such that the balance between exploration and exploitation is suitable for that state transition. The preliminary results of the proposed method is comparable with those from the literature.