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  • Open Access

    ARTICLE

    Optimization Techniques in University Timetabling Problem: Constraints, Methodologies, Benchmarks, and Open Issues

    Abeer Bashab1, Ashraf Osman Ibrahim2,*, Ibrahim Abakar Tarigo Hashem3, Karan Aggarwal4, Fadhil Mukhlif5, Fuad A. Ghaleb5, Abdelzahir Abdelmaboud6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6461-6484, 2023, DOI:10.32604/cmc.2023.034051

    Abstract University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as NP-COP, defining and clarifying the… More >

  • Open Access

    ARTICLE

    Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

    Erkan Akkur1, Fuat TURK2,*, Osman Erogul1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003

    Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >

  • Open Access

    ARTICLE

    Computational high-throughput screening and in vitro approaches identify CB-006-3; A novel PI3K-BRAFV600E dual targeted inhibitor against melanoma

    FAISAL HASSAN TOBEIGEI1, REEM M. GAHTANI2, AHMAD SHAIKH2, AMER AL ALI3, NADER KAMELI4,5, HOSSAM KAMLI2, PRASANNA RAJAGOPALAN2,6,*

    Oncology Research, Vol.29, No.5, pp. 305-318, 2021, DOI:10.32604/or.2022.025187

    Abstract Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase (PI3K), and RAS/BRAF pathways. In this work, through diversity-based high-throughput virtual screening we identified a lead molecule that selectively targets PI3K and BRAFV600E kinases. Computational screening, Molecular dynamics simulation and MMPBSA calculations were performed. PI3K and BRAFV600E kinase inhibition was done. A375 and G-361 cells were used for in vitro cellular analysis to determine antiproliferative effects, annexin V binding, nuclear fragmentation and cell cycle analysis. Computational screening of small molecules indicates compound CB-006-3 selectively targets PI3KCG (gamma subunit), PI3KCD (delta subunit) and BRAFV600E. Molecular dynamics simulation… More >

  • Open Access

    ARTICLE

    Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128

    Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized… More >

  • Open Access

    ARTICLE

    Routing with Cooperative Nodes Using Improved Learning Approaches

    R. Raja1,*, N. Satheesh2, J. Britto Dennis3, C. Raghavendra4

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2857-2874, 2023, DOI:10.32604/iasc.2023.026153

    Abstract In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, etc. The anticipated… More >

  • Open Access

    ARTICLE

    SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

    C. P. Thamil Selvi1,*, R. PushpaLakshmi2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2439-2456, 2023, DOI:10.32604/csse.2023.029254

    Abstract One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data… More >

  • Open Access

    ARTICLE

    Targeted Therapeutic Approaches in Vulvar Squamous Cell Cancer (VSCC): Case Series and Review of the Literature

    Linn Woelber*1, Sabrina Mathey*1, Katharina Prieske*†, Sascha Kuerti*, Christoph Hillen*, Eike Burandt, Anja Coym§, Volkmar Mueller*, Barbara Schmalfeldt*, Anna Jaeger*

    Oncology Research, Vol.28, No.6, pp. 645-659, 2020, DOI:10.3727/096504020X16076861118243

    Abstract Therapeutic options in recurrent or metastasized vulvar squamous cell cancer (VSCC) not amenable to radiotherapy or radical surgery are limited. Evidence for the use of targeted therapies is sparse. All patients with VSCC treated at the Gynecological Cancer Center Hamburg-Eppendorf 2013–2019 were retrospectively evaluated for targeted therapeutic approaches. Furthermore, a MEDLINE, EMBASE, Web of Science, Scopus, and OVID database search was performed using the terms: “vulvar cancer” AND “targeted therapy,” “erlotinib,” “EGFR,” “bevacizumab,” “VEGF,” “pembrolizumab,” or “immunotherapy.” Twelve of 291 patients (4.1%) with VSCC received at least one targeted therapy at our institution. Previously, one or more platinumbased chemotherapy was… More >

  • Open Access

    ARTICLE

    P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

    Ayman Altameem1, Ramesh Chandra Poonia2, Ankit Kumar3, Linesh Raja4, Abdul Khader Jilani Saudagar5,*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 553-566, 2023, DOI:10.32604/iasc.2023.027579

    Abstract Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to… More >

  • Open Access

    ARTICLE

    Content Based Automated File Organization Using Machine Learning Approaches

    Syed Ali Raza1,2, Sagheer Abbas1, Taher M. Ghazal3,4, Muhammad Adnan Khan5,6, Munir Ahmad1, Hussam Al Hamadi7,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1927-1942, 2022, DOI:10.32604/cmc.2022.029400

    Abstract In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in terms of content. The proposed… More >

  • Open Access

    ARTICLE

    Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

    S. Vinson Joshua1, A. Selwin Mich Priyadharson1, Raju Kannadasan2, Arfat Ahmad Khan3, Worawat Lawanont3,*, Faizan Ahmed Khan4, Ateeq Ur Rehman5, Muhammad Junaid Ali6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178

    Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields… More >

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