5,472 research outputs found
The Block Relocation Problem with Appointment Scheduling (BRPAS)
No full textThis Data set is used from different published papers in container/block relocation problems. The used instances are further processed to fit the research topic of "The Block Relocation Problem with Appointment Scheduling (BRPAS)". In the BRPAS, we introduce a new optimization problem that studies the container relocation in the container terminal under the appointment scheduling for container pickup operations. Since we are introducing a new problem, which is different from the existing work in the literature, we used some data sets from existing published work and modified them to fit our approach. However, the developed instances still can be comparable to the existing original instances. the uploaded files are named with authors' names of the original data set
First person – Ahmed Elbediwy
No full textFirst Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Ahmed Elbediwy is the first author on ‘Enigma proteins regulate YAP mechanotransduction’, published in Journal of Cell Science. Ahmed is a postdoctoral fellow in the lab of Barry Thompson at The Francis Crick Institute, London, UK, investigating the cytoskeleton, RhoGTPases and the regulation of YAP/TAZ oncoproteins
Hany Osman*; Ahmed Azab; Fazle Baki. (2023). Optimal Process Planning for Hybrid Additive and Subtractive Manufacturing. ASME Journal of Manufacturing Science and Engineering. Accepted.
No full textTasmia Tumpa*, Fazle Baki, Ahmed Azab. (2022). Robust Appointment Scheduling for Random Service Times Using Min-max Optimization. Advances in Operations Research. Submitted.
No full textAuthor Checklist - Full - Ahmed Essa_ 135540.pdf
No full textAttached is a required author checklist for animal research </p
Nusaiba AlTumi*, Anik Dash*, Hany Osman, Ahmed Azab, Fazle Baki. (2025). Multi-Mode Robust Appointment Scheduling for Uncertain Service Times and Random No-Shows Using Min-Max Optimization via a Hybridized Reinforcement-Learning Heuristic. Computers & Industrial Engineering. Submitted.
No full textAbul Mansur Ahmed Research Essay 2023
No full textThis is a research essay submitted to the 'Abul Mansur Ahmed Smriti Parishad' in 2023 for primary selection for publication, however, in the end, it was decided by the author not to publish it in the local publisher's policy. Thus the author holds the sole copyright of this article and possesses intellectual property of the work and wishes to publish it in future in reputable platforms.An Intellectual Work on Abul Mansur Ahmed produced by Naywaz Shari
The three blind men and the elephant. In search of a holistic view of Somalia. A comment to Ahmed I. Samatar
No full textIn this article, the author comments on Ahmed I. Samatar contribution on main perspectives gleaned from books and articles by Somali scholars published in the recent years.Qoraagu wuxuu maqaalkaan kaga faalloonayaa shirweynihi Axmed I. Samatar, oo la xiriiray waxyaabaha ugu doorka roon ee laga filayo in ay ka soo baxaan qoraalladii ay soo saareen aqoonyahannada Soomaaliyeed sannadaahan dambe.In questo articolo, l'autore commenta l'intervento al congresso di Ahmed I. Samatar relativo alle principali prospettive che emergono dai lavori pubblicati negli ultimi anni da studiosi somali.M.S. Lilius (ed.
Interview with Ahmed al-Dajani
No full textفي هذه المقابلة، يتحدث الدكتور أحمد الدجاني، المفكر الفلسطيني، عن الفصائل الفلسطينية. أجرت المقابلة إيمان رافع.In this interview, Ahmed al-Dajani, a Palestinian author, speaks about Palestinian factions. The interview was conducted by Iman Rafi
Simultaneous Twin Kernel Learning Using Polynomial Transformations for Structured Prediction
No full textMany learning problems in computer vision can be posed as structured prediction problems, where the input and output instances are structured objects such as trees, graphs or strings rather than, single labels {+1, −1} or scalars. Kernel methods such as Structured Support Vector Machines , Twin Gaussian Processes (TGP), Structured Gaussian Processes, and vector-valued Reproducing Kernel Hilbert Spaces (RKHS), offer powerful ways to perform learning and inference over these domains. Positive definite kernel functions allow us to quantitatively capture similarity between a pair of instances over these arbitrary domains. A poor choice of the kernel function, which decides the RKHS feature space, often results in poor performance. Automatic kernel selection methods have been developed, but have focused only on kernels on the input domain (i.e.’one-way’). In this work, we propose a novel and efficient algorithm for learning kernel functions simultaneously, on both input and output domains. We introduce the idea of learning polynomial kernel transformations, and call this method Simultaneous Twin Kernel Learning (STKL). STKL can learn arbitrary, but continuous kernel functions, including ’one-way’ kernel learning as a special case. We formulate this problem for learning covariances kernels of Twin Gaussian Processes. Our experimental evaluation using learned kernels on synthetic and several real-world datasets demonstrate consistent improvement in performance of TGP’s.Peer reviewe
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