Search results for: international ovarian tumor analysis classification

Authors: Ikediobi Lottanna Samuel

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Days are gone when perpetrators of core international crimes hide under the cloak of sovereignty to go with impunity. The principle of international criminal responsibility is a reality. This move to end impunity for violation of human rights has led to the creation of international and hybrid tribunals, a permanent international criminal court, and increased prosecution of human rights violations in domestic courts. This article examines the attempts by the international community to bring perpetrators of heinous crimes to book. The work reveals the inadequacy of the current international mechanism for prosecuting core international crimes in order to end the culture of impunity and entrench the culture of accountability. It also identifies that ad hoc international criminal tribunals and the international criminal court face similar challenges ranging from lack of cooperation by nation states, non-existence of hierarchy of crimes, lack of effective enforcement mechanism, limited prosecutorial capacity and agenda, difficulty in apprehending suspects, difficulty in blending different legal tradition, absence of a coherent sentencing guideline, distant location of courts, selective indictment, etc. These challenges adversely affect the functioning of these courts. It is suggested that a more helpful way to end impunity would be to have a more robust and synergistic relationship between national, regional, and international approaches to prosecuting core international crimes.

Keywords: prosecution, criminal, international, tribunal, justice, ad hoc

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Uche A. Nnawulezi

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This paper examines an essential and complex part of International humanitarian law standards of military necessity. Military necessity is an unpredictable phenomenon. The unpredictability of this regulation likewise originates from the fact that is one of the most fundamental, yet most misjudged and distorted standards of international law of armed conflict. This rule has been censured as essentially wrong in light of its non-compliance with the principles of international humanitarian law in recent past. The author noted in this study that military necessity runs counter to humanitarian exigencies. These have generated debate among researchers for them to propose that for international law to be considered more important, it is indispensable that the procedures and substance of custom be illuminated and made accessible to every one of the individuals who may utilize it or be influenced by it. However, a significant number of analysts have attributed particular weaknesses to this doctrine. This study relied on both primary and secondary sources of data collection. Significantly, the recommendation made in this paper, if completely adopted, shall go a long way in guaranteeing a better application of the principles of international humanitarian law.

Keywords: military necessity, international law, international humanitarian law, customary law

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Djehich Mohamed Yousri

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In this research paper, the future of international relations is believed to have an important place on the theoretical and applied levels because policy makers in the world are in dire need of such analyzes that are useful in drawing up the foreign policies of their countries, and protecting their national security from potential future threats, and in this context, The topic raised a lot of scientific controversy and intellectual debate, especially in terms of the extent of the effectiveness, accuracy, and ability of foresight methods to identify potential futures, and this is what attributed the controversy to the scientific foundations for foreseeing international relations. An arena for intellectual discussion between different thinkers in international relations belonging to different theoretical schools, which confirms to us the conceptual and implied development of prediction in order to reach the scientific level.

Keywords: foresight, forecasting, international relations, international relations theory, concept of international relations

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Authors: Daniel Suarez Alonso

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Cybercrime is a growing international threat and a challenge for law enforcement agencies and judicial systems worldwide. International cooperation is necessary to solve this problem because cybercrime knows no borders and often involves multiple jurisdictions, being related to organised crime. The purpose of this article is to analyse international cooperation in the investigation and prosecution of cybercrime, focusing on the framework of the Regulation of the European Union Agency for Law Enforcement Cooperation (EUROPOL), cooperation that takes place between police authorities from different countries. It examines the legal and operational mechanisms in place to facilitate international cooperation in Europe in this area and assesses their effectiveness in the fight against cybercrime. In addition, the study of a Spanish investigation where cooperation with EUROPOL took place will be examined, analyzing how international cooperation was carried out to investigate and track down criminals. Lessons learned from this case will be discussed and recommendations for improving international cooperation in the fight against cybercrime will be proposed.

Keywords: Europol, international cooperation, cybercrime, computer crime, law

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Authors: F. Kashanian, M. M. Masoudi, A. Akbari, A. Shamloo, M. R. Zand, S. S. Salehi

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Nano-sized materials present new opportunities in biology and medicine and they are used as biomedical tools for investigation, separation of molecules and cells. To achieve more effective cancer therapy, it is essential to select cancer cells exactly. This research suggests that using the antibody-functionalized nontoxic Arginine-doped magnetic nanoparticles (A-MNPs), has been prosperous in detection, capture, and magnetic separation of circulating tumor cells (CTCs) in tumor tissue. In this study, A-MNPs were synthesized via a simple precipitation reaction and directly immobilized Ep-CAM EBA-1 antibodies over superparamagnetic A-MNPs for Mucin BCA-225 in breast cancer cell. The samples were characterized by vibrating sample magnetometer (VSM), FT-IR spectroscopy, Tunneling Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). These antibody-functionalized nontoxic A-MNPs were used to capture breast cancer cell. Through employing a strong permanent magnet, the magnetic separation was achieved within a few seconds. Antibody-Conjugated nontoxic Arginine-doped Fe₃O₄ nanoparticles have the potential for the future study to capture CTCs which are released from tumor tissue and for drug delivery, and these results demonstrate that the antibody-conjugated A-MNPs can be used in magnetic hyperthermia techniques for cancer treatment.

Keywords: tumor tissue, antibody, magnetic nanoparticle, CTCs capturing

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Akram Boubakri, Fethi Choubani, Tan Hoa Vuong, Jacques David

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Nowadays, microwave hyperthermia is considered as an effective treatment for the malignant tumors. This microwave treatment which comes to substitute the chemotherapy and the surgical intervention enables an in-depth tumor heating without causing any diseases to the sane tissue. This technique requires a high precision system, in order to effectively concentrate the heating just in the tumor, without heating any surrounding healthy tissue. In the hyperthermia treatment, the temperature in cancerous area is typically raised up to over 42◦C and maintained for one hour in order to destroy the tumor sufficiently, whilst in the surrounding healthy tissues, the temperature is maintained below 42◦C to avoid any damage. Metamaterial lenses are widely used in medical applications like microwave hyperthermia treatment. They enabled a subdiffraction resolution thanks to the amplification of the evanescent waves and they can focus electromagnetic waves from a point source to a point image. Metasurfaces have been used to built metamaterial lenses. The main mechanical advantages of those structures over three dimensional material structures are ease of fabrication and a smaller required volume. Here in this work, we proposed a metasurface based lens operating at the frequency of 6 GHz and designed for microwave hyperthermia. This lens was applied and showed good results in focusing and heating the tumor inside a breast tissue with an increased and maintained temperature above 42°C. The tumor was placed in the focal distance of the lens so that only the tumor tissue will be heated. Finally, in this work, it has been shown that the hyperthermia area within the tissue can be carefully adjusted by moving the antennas or by changing the thickness of the metamaterial lenses based on the tumor position. Even though the simulations performed in this work have taken into account an ideal case, some real characteristics can be considered to improve the obtained results in a realistic model.

Keywords: focusing, hyperthermia, metamaterial lenses, metasurface, microwave treatment

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Authors: Wooseok Byon, Eunyoung Kim, Junseong Kwon, Byung Joo Song, Chan Heun Park

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Background/Purpose: Previous preclinical and clinical data suggest that increased lymphocytic infiltration would be associated with good prognosis and benefit from immunogenic chemotherapy especially in triple-negative breast cancer (TNBC). We investigated a single-center experience of TNBC and relationship with lymphocytic infiltration. Methods: From January 2004 to December 2012, at the Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University, School of Medicine, we retrospectively reviewed 897 breast cancer patients-clinical outcomes, clinicopathological characteristics, breast cancer subtypes. And we reviewed lymphocytic infiltration of TNBC specimens by two pathologists. Statistical analysis of risk factors associated with recurrence was performed. Results: A total of 897 patients, 76 were TNBC (8.47%). Mean age of TNBC patients were 50.95 (SD10.42) years, mean follow-up periods was 40.06 months. We reviewed 49 slides, and there were 8 recurrent breast cancer patients (16.32%), and 4 patients were expired (8.16%). There were 9 lymphocytic predominant breast cancers (LPBC)-carcinomas with either intratumoral lymphocytes in >60% of tumor cell nests. 1 patient of LPBC was recurred and 8 were not. In multivariate logistic regression, the odds ratio of lymphocytic infiltration was 0.59 (p=0.643). Conclusion: In a single-center experience of TNBC, the lymphocytic infiltration in tumor cell nest might be a good trend on the prognosis but there was not statistically significant.

Keywords: tumor-infiltrating lymphocytes, triple negative breast cancer, medical and health sciences

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Authors: Kolja Them, Priyal Chikhaliwala, Sudeshna Chandra

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Purpose: The purpose of this work is to examine possibilities for noninvasive imaging and identification of tumor markers for cancer diagnosis. The proposed method uses antibody conjugated iron oxide nanoparticles and multicolor Magnetic Particle Imaging (mMPI). The method has the potential for radiation exposure free real-time estimation of local tumor marker concentrations in vivo. In this study, the method is applied to human Alpha-1-Fetoprotein. Materials and Methods: As tracer material AFP antibody-conjugated Dendrimer-Fe3O4 nanoparticles were used. The nanoparticle bioconjugates were then incubated with bovine serum albumin (BSA) to block any possible nonspecific binding sites. Parts of the resulting solution were then incubated with AFP antigen. MPI measurements were done using the preclinical MPI scanner (Bruker Biospin MRI GmbH) and the multicolor method was used for image reconstruction. Results: In multicolor MPI images the nanoparticles incubated only with BSA were clearly distinguished from nanoparticles incubated with BSA and AFP antigens. Conclusion: Tomographic imaging of human tumor marker Alpha-1-Fetoprotein is possible using AFP antibody conjugated iron oxide nanoparticles in presence of BSA. This opens interesting perspectives for cancer diagnosis.

Keywords: noninvasive imaging, tumor antigens, antibody conjugated iron oxide nanoparticles, multicolor magnetic particle imaging, cancer diagnosis

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

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In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

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Authors: Taoues Aimeur

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The present study is the first comparative study of discourses surrounding women on International Women’s Day in French-language newspapers and Arabic-language newspapers in Algeria. It aims at critically examining the way women are positioned on International Women’s Day in four Algerian newspapers by focusing on the post-civil war era in Algeria (2003 till the present time). This is by applying Feminist Critical Discourse Analysis to question representations of women in the selected newspapers by revealing the gender ideologies embedded in their linguistic and visual discourses. The Francophone newspapers chosen for the present research are El Watan and Liberté. As for the Arabophone ones, El Khabar and Echorouk have been selected. The results of the study would help build an understanding of the meanings of gender that are embedded in the discourses of the selected news outlets which differ both linguistically and ideologically.

Keywords: Arabic-language newspapers, Critical Discourse Analysis, discourses, French-language newspapers, gender, International Women’s Day

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Authors: Meitham Amereh, Mohsen Akbari, Ben Nadler

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Cancer has been recognized as one of the most challenging problems in biology and medicine. Aggressive tumors are a lethal type of cancers characterized by high genomic instability, rapid progression, invasiveness, and therapeutic resistance. Their behavior involves complicated molecular biology and consequential dynamics. Although tremendous effort has been devoted to developing therapeutic approaches, there is still a huge need for new insights into the dark aspects of tumors. As one of the key requirements in better understanding the complex behavior of tumors, mathematical modeling and continuum physics, in particular, play a pivotal role. Mathematical modeling can provide a quantitative prediction on biological processes and help interpret complicated physiological interactions in tumors microenvironment. The pathophysiology of aggressive tumors is strongly affected by the extracellular cues such as stresses produced by mechanical forces between the tumor and the host tissue. During the tumor progression, the growing mass displaces the surrounding extracellular matrix (ECM), and due to the level of tissue stiffness, stress accumulates inside the tumor. The produced stress can influence the tumor by breaking adherent junctions. During this process, the tumor stops the rapid proliferation and begins to remodel its shape to preserve the homeostatic equilibrium state. To reach this, the tumor, in turn, upregulates epithelial to mesenchymal transit-inducing transcription factors (EMT-TFs). These EMT-TFs are involved in various signaling cascades, which are often associated with tumor invasiveness and malignancy. In this work, we modeled the tumor as a growing hyperplastic mass and investigated the effects of mechanical stress from surrounding ECM on tumor invasion. The invasion is modeled as volume-preserving inelastic evolution. In this framework, principal balance laws are considered for tumor mass, linear momentum, and diffusion of nutrients. Also, mechanical interactions between the tumor and ECM is modeled using Ciarlet constitutive strain energy function, and dissipation inequality is utilized to model the volumetric growth rate. System parameters, such as rate of nutrient uptake and cell proliferation, are obtained experimentally. To validate the model, human Glioblastoma multiforme (hGBM) tumor spheroids were incorporated inside Matrigel/Alginate composite hydrogel and was injected into a microfluidic chip to mimic the tumor’s natural microenvironment. The invasion structure was analyzed by imaging the spheroid over time. Also, the expression of transcriptional factors involved in invasion was measured by immune-staining the tumor. The volumetric growth, stress distribution, and inelastic evolution of tumors were predicted by the model. Results showed that the level of invasion is in direct correlation with the level of predicted stress within the tumor. Moreover, the invasion length measured by fluorescent imaging was shown to be related to the inelastic evolution of tumors obtained by the model.

Keywords: cancer, invasion, mathematical modeling, microfluidic chip, tumor spheroids

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Authors: Rozhgar A. Khailany, Mehri Igci, Emine Bayraktar, Sakip Erturhan, Metin Karakok, Ahmet Arslan

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Kidney cancer is the most lethal urological cancer accounting for 3% of adult malignancies. VHL, a tumor-suppressor gene, is best known to be associated with renal cell carcinoma (RCC). The VHL functions as negative regulator of hypoxia inducible factors. Recent sequencing efforts have identified several novel frequent mutations of histone modifying and chromatin remodeling genes in ccRCC (clear cell RCC) including PBRM1 and SETD2. The PBRM1 gene encodes the BAF180 protein, which involved in transcriptional activation and repression of selected genes. SETD2 encodes a histone methyltransferase, which may play a role in suppressing tumor development. In this study, RNAs of 30 paired tumor and normal samples that were grouped according to the types of kidney cancer and clinical characteristics of patients, including gender and average age were examined by RT-PCR, SSCP and sequencing techniques. VHL, PBRM1 and SETD2 expressions were relatively down-regulated. However, statistically no significance was found (Wilcoxon signed rank test, p > 0.05). Interestingly, no mutation was observed on the contrary of previous studies. Understanding the molecular mechanisms involved in the pathogenesis of RCC has aided the development of molecular-targeted drugs for kidney cancer. Further analysis is required to identify the responsible genes rather than VHL, PBRM1 and SETD2 in kidney cancer.

Keywords: kidney cancer, molecular biomarker, expression analysis, mutation screening

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Authors: Rizwan Iqbal

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Introduction: Re-TURBT has been recommended by international guidelines for patients with non-muscle invasive bladder cancer (NMIBC) who are deemed high-risk. Indications for re-TURBTs remain controversial and studies show mixed outcomes. It should be performed when the initial TURBT specimen lacks detrusor muscle, has tumor stage pT1 or G3/high-grade, or where resection is deemed incomplete. This ensures complete resection of tumors that have a high risk of recurrence as well as accurately identifying any tumors which have been upstaged. The aim of this audit was to evaluate the quality of re-TURBTs in a district general hospital. Method: Data were retrospectively collected from 31 patients who had re-TURBTs between April 2021 and September 2022. Data included baseline demographics, time from initial to re-TURBT, quality of operation note, presence of residual tumor, complications, and administration of chemotherapy within 24 hours of the initial TURBT. Data collection remains ongoing at the time of writing. Results: The mean age was 76 years old and 71.0% of patients were male. 32.3% of patients had their re-TURBT within six weeks and 32.3% had intravesical chemotherapy administered within 24 hours of the initial TURBT. 74.2% of initial TURBTs had detrusor muscle present in the specimen. 48.4% of patients had residual disease following re-TURBT. Just one patient had their pathology upstaged at re-TURBT. The use of the TURBT proforma on the operation note was variable, with 51.6% and 38.7% of surgeons using the proforma after the initial and re-TURBT. Conclusion: Re-TURBT improves bladder cancer staging and is necessary in patients who are deemed high-risk in order to identify any upstaging or recurrence of the disease.

Keywords: urology, bladder cancer, turbt, cancer

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Authors: Semanur Soyyigit Kaya, Ercan Eren

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Energy has a prominent role for development of nations. Countries which have energy resources also have strategic power in the international trade of energy since it is essential for all stages of production in the economy. Thus, it is important for countries to analyze the weakness and strength of the system. On the other side, it is commonly believed that international trade has complex network properties. Complex network is a tool for the analysis of complex systems with heterogeneous agents and interaction between them. A complex network consists of nodes and the interactions between these nodes. Total properties which emerge as a result of these interactions are distinct from the sum of small parts (more or less) in complex systems. Thus, standard approaches to international trade are superficial to analyze these systems. Network analysis provides a new approach to analyze international trade as a network. In this network countries constitute nodes and trade relations (export or import) constitute edges. It becomes possible to analyze international trade network in terms of high degree indicators which are specific to complex systems such as connectivity, clustering, assortativity/disassortativity, centrality, etc. In this analysis, international trade of crude oil and coal which are types of fossil fuel has been analyzed from 2005 to 2014 via network analysis. First, it has been analyzed in terms of some topological parameters such as density, transitivity, clustering etc. Afterwards, fitness to Pareto distribution has been analyzed. Finally, weighted HITS algorithm has been applied to the data as a centrality measure to determine the real prominence of countries in these trade networks. Weighted HITS algorithm is a strong tool to analyze the network by ranking countries with regards to prominence of their trade partners. We have calculated both an export centrality and an import centrality by applying w-HITS algorithm to data.

Keywords: complex network approach, fossil fuel, international trade, network theory

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Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Leone Sherman

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While the democratic rights of a citizen may be very clearly outlined in a country’s constitution, it’s not uncommon for political elite to undermine those rights and gain more power and control over a country than it is allowed by this constitution. Moreover, while such a change in some smaller states may not have a substantial impact on the international community, the same change in countries with vast resources and political influence, such as Russia, is always a considerable factor for the world policy. This article aims to research the weaknesses of the Russian democratic system and their effect on the international policy through the three key aspects: The Russian people’s ability to produce the required political will to control their government’s decisions, the current development of the Russian political environment, and the affection of this environment on the world community as a whole during the recent years. The used methodology is a narrative analysis of recent political events, official statistics, international investigations and media statements. As a result, the ever-widening gap between the people and the government becomes evidently seen, as well as the challenges it imposes on the political world arena, both current and those that still lie ahead of us.

Keywords: Russia, political analysis, democratic weaknesses, international society

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Authors: Chang Liang, Weizhi Gong, Yan Zhang

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Purpose: Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis worldwide. Murine double minute 2 (MDM2) regulates the tumor suppressor p53, increasing cancer risk and accelerating tumor progression. Speckle-type POX virus and zinc finger protein (SPOP), a key of subunit of Cullin-Ring E3 ligase, inhibits tumor genesis and progression by the ubiquitination of its downstream substrates. This study aimed to clarify whether SPOP and MDM2 are mutually regulated in HCC and the correlation between SPOP and MDM2 and the prognosis of HCC patients. Methods: First, the expression of SPOP and MDM2 in HCC tissues were detected by TCGA database. Then, 53 paired samples of HCC tumor and adjacent tissues were collected to evaluate the expression of SPOP and MDM2 using immunohistochemistry. Chi-square test or Fisher’s exact test were used to analyze the relationship between clinicopathological features and the expression levels of SPOP and MDM2. In addition, Kaplan‒Meier curve analysis and log-rank test were used to investigate the effects of SPOP and MDM2 on the survival of HCC patients. Last, the Multivariate Cox proportional risk regression model analyzed whether the different expression levels of SPOP and MDM2 were independent risk factors for the prognosis of HCC patients. Results: Bioinformatics analysis revealed the low expression of SPOP and high expression of MDM2 were related to worse prognosis of HCC patients. The relationship between the expression of SPOP and MDM2 and tumor stem-like features showed an opposite trend. The immunohistochemistry showed the expression of SPOP protein was significantly downregulated while MDM2 protein significantly upregulated in HCC tissue compared to that in para-cancerous tissue. Tumors with low SPOP expression were related to worse T stage and Barcelona Clinic Liver Cancer (BCLC) stage, but tumors with high MDM2 expression were related to worse T stage, M stage, and BCLC stage. Kaplan–Meier curves showed HCC patients with high SPOP expression and low MDM2 expression had better survival than those with low SPOP expression and high MDM2 expression (P < 0.05). A multivariate Cox proportional risk regression model confirmed that a high MDM2 expression level was an independent risk factor for poor prognosis in HCC patients (P <0.05). Conclusion: The expression of SPOP protein was significantly downregulated, while the expression of MDM2 significantly upregulated in HCC. The low expression of SPOP and high expression. of MDM2 were associated with malignant progression and poor prognosis of HCC patients, indicating a potential therapeutic target for HCC patients.

Keywords: hepatocellular carcinoma, murine double minute 2, speckle-type POX virus and zinc finger protein, ubiquitination

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Emma Charlene Lubaale

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The International Criminal Court (ICC) operates based on the principle of complementarity. On the basis of this principle, states enjoy the primary right to prosecute international crimes, with the ICC intervening only when a state with jurisdiction over an international crime is unable or unwilling to prosecute. To ably exercise their primary right to prosecute international crimes domestically, a number of states are taking steps to criminalise international crimes in their national laws. Significant to note, many of the laws enacted are not being applied in the prosecution of the international crimes allegedly committed. Kenya, Uganda and Sudan are some notable states where commission of international crimes is documented. All these states have recently enacted laws on international crimes. Kenya enacted the International Crimes Act in 2008, Uganda enacted the International Criminal Court Act in 2010 and in 2007, Sudan made provision for international crimes under its Armed Forces Act. However, in all these three states, the enacted national laws on international crimes have thus far not featured in any of the proceedings before these states’ courts. Instead, these states have either relied on ordinary crimes to prosecute international crimes or not prosecuted international crimes altogether. This paper underscores the limitations of the enacted laws, explaining why, even with efforts taken by these states to enact national laws on international crimes, these laws cannot be relied on to advance accountability for the international crimes. Notably, the laws in Kenya and Uganda do not have retroactive application. In Sudan, despite the 2007 reforms, the structure of military justice in Sudan has the effect of placing certain categories of individuals beyond the reach of international criminal justice. For Kenya and Uganda, it is concluded that the only benefit that flows from these enactments is reliance on them to prosecute future international crimes. For Sudan, the 2007 reforms will only have the desired impact if reforms are equally made to the structure of military justice.

Keywords: complementarity, national laws, Kenya, Sudan, Uganda, international crimes, limitations

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

Procedia PDF Downloads 393

Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

Procedia PDF Downloads 401

Authors: Qamar Iqbal

Abstract:

Companies strive to improve their forecasting methods to predict the fluctuations in customer demand. These fluctuation and variation in demand affect the manufacturing operations and can limit a company’s ability to fulfill customer demand on time. Companies keep the inventory buffer and maintain the stocking levels to reduce the impact of demand variation. A mid-size company deals with thousands of stock keeping units (skus). It is neither easy and nor efficient to control and manage each sku. Inventory classification provides a tool to the management to increase their ability to support customer demand. The paper presents a framework that shows how inventory classification can play a role to increase supply chain responsiveness. A case study will be presented to further elaborate the method both for build-to-order and build-to-forecast manufacturing environments. Results will be compared that will show which manufacturing setting has advantage over another under different circumstances. The outcome of this study is very useful to the management because this will give them an insight on how inventory classification can be used to increase their ability to respond to changing customer needs.

Keywords: inventory classification, supply chain responsiveness, forecast, manufacturing environment

Procedia PDF Downloads 595