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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Open Access
Evolutionary multiobjective feature selection for sentiment analysis
(Institute of Electrical and Electronics Engineers (IEEE), 2021) Pelin Angın; Deniz, Ayça; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
Sentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space.
Open Access
Thiophene-based trimers for in vivo electronic functionalization of tissues
(American Chemical Society (ACS), 2020) Mantione, Daniele; Dufil, Gwennael; Vallan, Lorenzo; Parker, Daniela; Brochon, Cyril; Cloutet, Eric; Hadziioannou, Georges; Berggren, Magnus; Stavrinidou, Eleni; Pavlopoulou, Eleni; Department of Mechanical Engineering; İstif, Emin; Faculty Member; Master Student; Department of Mechanical Engineering; College of Engineering
Electronic materials that can self-organize in vivo and form functional components along the tissue of interest can result in a seamless integration of the bioelectronic interface. Previously, we presented in vivo polymerization of the conjugated oligomer ETE-S in plants, forming conductors along the plant structure. The EDOT-thiophene-EDOT trimer with a sulfonate side group polymerized due to the native enzymatic activity of the plant and integrated within the plant cell wall. Here, we present the synthesis of three different conjugated trimers based on thiophene and EDOT or purely EDOT trimers that are able to polymerize enzymatically in physiological pH in vitro as well as in vivo along the roots of living plants. We show that by modulating the backbone and the side chain, we can tune the electronic properties of the resulting polymers as well as their localization and penetration within the root. Our work paves the way for the rational design of electronic materials that can self-organize in vivo for spatially controlled electronic functionalization of living tissue.
Open Access
Introduction to noise radar and its waveforms
(Multidisciplinary Digital Publishing Institute (MDPI), 2020) De Palo, Francesco; Galati, Gaspare; Pavan, Gabriele; Wasserzier, Christoph; Department of Electrical and Electronics Engineering; Savcı, Kubilay; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering
In the system-level design for both conventional radars and noise radars, a fundamental element is the use of waveforms suited to the particular application. In the military arena, low probability of intercept (LPI) and of exploitation (LPE) by the enemy are required, while in the civil context, the spectrum occupancy is a more and more important requirement, because of the growing request by non-radar applications; hence, a plurality of nearby radars may be obliged to transmit in the same band. All these requirements are satisfied by noise radar technology. After an overview of the main noise radar features and design problems, this paper summarizes recent developments in "tailoring" pseudo-random sequences plus a novel tailoring method aiming for an increase of detection performance whilst enabling to produce a (virtually) unlimited number of noise-like waveforms usable in different applications.
Open Access
Effects of force field selection on the computational ranking of MOFs for CO2 separations
(American Chemical Society (ACS), 2018) Department of Chemical and Biological Engineering; Keskin, Seda; Dokur, Derya; Master Student; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; 40548; N/A
Metal-organic frameworks (MOFs) have been considered as highly promising materials for adsorption-based CO2 separations. The number of synthesized MOFs has been increasing very rapidly. High-throughput molecular simulations are very useful to screen large numbers of MOFs in order to identify the most promising adsorbents prior to extensive experimental studies. Results of molecular simulations depend on the force field used to define the interactions between gas molecules and MOFs. Choosing the appropriate force field for MOFs is essential to make reliable predictions about the materials' performance. In this work, we performed two sets of molecular simulations using the two widely used generic force fields, Dreiding and UFF, and obtained adsorption data of CO2/H-2, CO2/N-2, and CO2/CH4 mixtures in 100 different MOF structures. Using this adsorption data, several adsorbent evaluation metrics including selectivity, working capacity, sorbent selection parameter, and percent regenerability were computed for each MOF. MOFs were then ranked based on these evaluation metrics, and top performing materials were identified. We then examined the sensitivity of the MOF rankings to the force field type. Our results showed that although there are significant quantitative differences between some adsorbent evaluation metrics computed using different force fields, rankings of the top MOF adsorbents for CO2 separations are generally similar: 8, 8, and 9 out of the top 10 most selective MOFs were found to be identical in the ranking for CO2/H-2, CO2/N-2, and CO2/CH4 separations using Dreiding and UFF. We finally suggested a force field factor depending on the energy parameters of atoms present in the MOFs to quantify the robustness of the simulation results to the force field selection. This easily computable factor will be highly useful to determine whether the results are sensitive to the force field type or not prior to performing computationally demanding molecular simulations.
Open Access
On the physical design of molecular communication receiver based on nanoscale biosensors
(Institute of Electrical and Electronics Engineers (IEEE), 2016) Department of Electrical and Electronics Engineering; Kuşcu, Murat; Akan, Özgür Barış; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering
Molecular communications, where molecules are used to encode, transmit, and receive information, are a promising means of enabling the coordination of nanoscale devices. The paradigm has been extensively studied from various aspects, including channel modeling and noise analysis. Comparatively little attention has been given to the physical design of molecular receiver and transmitter, envisioning biological synthetic cells with intrinsic molecular reception and transmission capabilities as the future nanomachines. However, this assumption leads to a discrepancy between the envisaged applications requiring complex communication interfaces and protocols, and the very limited computational capacities of the envisioned biological nanomachines. In this paper, we examine the feasibility of designing a molecular receiver, in a physical domain other than synthetic biology, meeting the basic requirements of nanonetwork applications. We first review the state-of-the-art biosensing approaches to determine whether they can inspire a receiver design. We reveal that the nanoscale field effect transistor-based electrical biosensor technology (bioFET) is particularly a useful starting point for designing a molecular receiver. Focusing on bioFET-based molecular receivers with a conceptual approach, we provide a guideline elaborating on their operation principles, performance metrics, and design parameters. We then provide a simple model for signal flow in silicon nanowire FET-based molecular receiver. Finally, we discuss the practical challenges of implementing the receiver and present the future research avenues from a communication theoretical perspective.
Open Access
A diversity combination model incorporating an inward bias for interaural time-level difference cue integration in sound lateralization
(Multidisciplinary Digital Publishing Institute (MDPI), 2020) N/A; Department of Computer Engineering; Mojtahedi, Sina; Erzin, Engin; Ungan, Pekcan; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; 34503; N/A
A sound source with non-zero azimuth leads to interaural time level differences (ITD and ILD). Studies on hearing system imply that these cues are encoded in different parts of the brain, but combined to produce a single lateralization percept as evidenced by experiments indicating trading between them. According to the duplex theory of sound lateralization, ITD and ILD play a more significant role in low-frequency and high-frequency stimulations, respectively. In this study, ITD and ILD, which were extracted from a generic head-related transfer functions, were imposed on a complex sound consisting of two low- and seven high-frequency tones. Two-alternative forced-choice behavioral tests were employed to assess the accuracy in identifying a change in lateralization. Based on a diversity combination model and using the error rate data obtained from the tests, the weights of the ITD and ILD cues in their integration were determined by incorporating a bias observed for inward shifts. The weights of the two cues were found to change with the azimuth of the sound source. While the ILD appears to be the optimal cue for the azimuths near the midline, the ITD and ILD weights turn to be balanced for the azimuths far from the midline.
Open Access
Finger-actuated microneedle array for sampling body fluids
(Multidisciplinary Digital Publishing Institute (MDPI), 2021) Ahmadpour, Abdollah; Yetişen, Ali K.; Department of Mechanical Engineering; Taşoğlu, Savaş; Sarabi, Misagh Rezapour; Faculty Member; Department of Mechanical Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; Graduate School of Sciences and Engineering; 291971; N/A
The application of microneedles (MNs) for minimally invasive biological fluid sampling is rapidly emerging, offering a user-friendly approach with decreased insertion pain and less harm to the tissues compared to conventional needles. Here, a finger-powered microneedle array (MNA) integrated with a microfluidic chip was conceptualized to extract body fluid samples. Actuated by finger pressure, the microfluidic device enables an efficient approach for the user to collect their own body fluids in a simple and fast manner without the requirement for a healthcare worker. The processes for extracting human blood and interstitial fluid (ISF) from the body and the flow across the device, estimating the amount of the extracted fluid, were simulated. The design in this work can be utilized for the minimally invasive personalized medical equipment offering a simple usage procedure.
Open Access
A queueing-theoretical delay analysis for intra-body nervous nanonetwork
(Elsevier, 2015) Department of Electrical and Electronics Engineering; Abbasi, Naveed Ahmed; Akan, Özgür Barış; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering
Nanonetworks is an emerging field of study where nanomachines communicate to work beyond their individual limited processing capabilities and perform complicated tasks. The human body is an example of a very large nanoscale communication network, where individual constituents communicate by means of molecular nanonetworks. Amongst the various intra-body networks, the nervous system forms the largest and the most complex network. In this paper, we introduce a queueing theory based delay analysis model for neuro-spike communication between two neurons. Using standard queueing model blocks such as servers, queues and fork-join networks, impulse reception and processing through the nervous system is modeled as arrival and service processes in queues. Simulations show that the response time characteristics of the model are comparable to those of the biological neurons.
Open Access
Automatic CNN-based Arabic numeral spotting and handwritten digit recognition by using deep transfer learning in Ottoman population registers
(Multidisciplinary Digital Publishing Institute (MDPI), 2020) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.
Open Access
Detection in molecular communications with ligand receptors under molecular interference
(Elsevier, 2021) Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Kuşcu, Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 6647; 316349
Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to transfer information among bio-nano devices. In this paper, we focus on the detection problem for biological MC receivers employing ligand receptors to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. In practice, receptors are not ideally selective against target ligands, and in physiological environments, they can interact with multiple types of ligands at different reaction rates depending on their binding affinity. This molecular cross-talk can cause a substantial interference on MC. Here we consider a particular scenario, where there is non-negligible concentration of interferer molecules in the channel, which have similar receptor-binding characteristics with the information molecules, and the receiver employs single type of receptors. We investigate the performance of four different detection methods, which make use of different statistics of the ligand-receptor binding reactions: instantaneous number of bound receptors, unbound time durations of receptors, bound time durations of receptors, and combination of unbound and bound time durations of receptors within a sampling time interval. The performances of the introduced detection methods are evaluated in terms of bit error probability for varying strength of molecular interference, similarity between information and interferer molecules, number of receptors, and received concentration difference between bit-0 and bit-1 transmissions. We propose synthetic receptor designs that can convert the required receptor statistics to the concentration of intracellular molecules, and chemical reaction networks that can chemically perform the computations required for detection.

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