Search results for: binary classifier

Authors: Yibeltal T. Bayou, Yohana S. Mashalla, Gloria Thupayagale-Tshweneagae

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Background: Maternal mortality has been shown to be lower in urban areas than in rural areas. However, disparities for the fast-growing population of urban poor who struggle as much their rural counterparts to access quality healthcare are masked by the urban averages. The aim of this paper is to report on the findings of antenatal adequacy among slum residents in Addis Ababa, Ethiopia. Methods and Materials: A quantitative and cross-sectional community-based study design was employed. A stratified two-stage cluster sampling technique was used to determine the sample and data was collected using structured questionnaire administered to 837 women aged 15-49 years. Binary logistic regression models were employed to identify predictors of adequacy of antenatal care. Results: The majority of slum residents did not have adequate antenatal care services i.e., only 50.7%, 19.3% and 10.2% of the slum resident women initiated early antenatal care, received adequate antenatal care service contents and had overall adequate antenatal care services. Pregnancy intention, educational status and place of ANC visits were important determinant factors for adequacy of ANC in the study area. Women with secondary and above educational status were 2.9 times more likely to have overall adequate care compared to those with no formal education. Similarly, women whose last pregnancy was intended and clients of private healthcare facilities were 1.8 and 2.8 times more likely to have overall adequate antenatal care compared to those whose last pregnancy was unintended and clients of public healthcare facilities respectively. Conclusion: In order to improve ANC adequacy in the study area, the policymaking, planning, and implementation processes should focus on the poor adequacy of ANC among the disadvantaged groups in particular and the slum residents in general.

Keywords: Addis Ababa, adequacy of antenatal care, slum residents, maternal mortality

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Authors: E. Feizi Ashtiani, S. Jamshidi, M.H Niksokhan, A. Feizi Ashtiani

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Waste load allocation (WLA) policies may use multi-objective optimization methods to find the most appropriate and sustainable solutions. These usually intend to simultaneously minimize two criteria, total abatement costs (TC) and environmental violations (EV). If other criteria, such as inequity, need for minimization as well, it requires introducing more binary optimizations through different scenarios. In order to reduce the calculation steps, this study presents value index as an innovative decision making approach. Since the value index contains both the environmental violation and treatment costs, it can be maximized simultaneously with the equity index. It implies that the definition of different scenarios for environmental violations is no longer required. Furthermore, the solution is not necessarily the point with minimized total costs or environmental violations. This idea is testified for Haraz River, in north of Iran. Here, the dissolved oxygen (DO) level of river is simulated by Streeter-Phelps equation in MATLAB software. The WLA is determined for fish farms using multi-objective particle swarm optimization (MOPSO) in two scenarios. At first, the trade-off curves of TC-EV and TC-Inequity are plotted separately as the conventional approach. In the second, the Value-Equity curve is derived. The comparative results show that the solutions are in a similar range of inequity with lower total costs. This is due to the freedom of environmental violation attained in value index. As a result, the conventional approach can well be replaced by the value index particularly for problems optimizing these objectives. This reduces the process to achieve the best solutions and may find better classification for scenario definition. It is also concluded that decision makers are better to focus on value index and weighting its contents to find the most sustainable alternatives based on their requirements.

Keywords: waste load allocation (WLA), value index, multi objective particle swarm optimization (MOPSO), Haraz River, equity

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Authors: Alongkorn Pekalee, Rossarin Gray

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Various determinants associated with older health include socio-demographic factors and health-promoting behaviors but lack in scholars recommended what factors associated with health status in specific sub-groups of older adults. The current study aims to explore the health-promoting behaviors and to examine and compare the associations of these factors with self-rated health status among three older age cohorts in Thai traditional context. Methods: This study is based on the Survey of Older Persons in Thailand (SOPT), in 2017, conducted by the National Statistical Office (NSO) of Thailand. Participants were classified into three groups by using the Thai contextual recommendation: youngest-old cohort (60-69), old-old cohort (70-79) and oldest old cohort (80 or older). Health promoting behaviors are the behaviors which associated with the health status of older adults include alcohol consumption, smoking, diet, and physical activity. Health status was defined as a subjective measurement by using self-rated health, a simple measure of general health. The socio-demographic factors, health-promoting behaviors, and health status were explained and summarized by descriptive statistics. The binary logistic regression was performed to analyze the data and evaluate the associations between independent and dependent variables. Results: Increase of age contributes to a higher proportion of health-promoting behaviors. All variables were associated with self-reported health status as good health among three older age cohorts statistically significant (p-value = 0.000). However, the influence of income sufficiency on health status is more notable, especially in older adults who aged 60-69 and 70-79. The influence of dietary and physical activity on health status became greater as age increased. Conclusion: the results suggest that income sufficiency should be noted in a plan to promote healthy aging, and co-residence should be more concerned especially in the oldest old cohort. Moreover, the interventions or policies to promote older health behaviors like diet and physical activity should be emphasized in the oldest old cohort more than others.

Keywords: health-promoting behaviors, older adults, self- rated health, Thailand

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Authors: Elucir Gir, Laelson Rochelle Milanês Sousa, Renata Karina Reis, Soraia Assad Nasbine Rabeh, Mayra Gonçalves Menegueti, Ana Cristina de Oliveira e Silva, Sheila Araújo Teles

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Context and significance: Nursing team professionals faced challenges in combating the COVID-19 pandemic around the world. They were subjected to exhausting workloads and prolonged use of Personal Protective Equipment. Using N95 masks for long periods of time can cause skin changes. In this context, health professionals who worked on the front lines of fighting the pandemic were more exposed to possible physical and psychological changes. Objective: The aim of the study was to analyze the factors associated with skin lesions resulting from the use of N95 masks among nursing team professionals. Method: The study was carried out in all regions of Brazil from October to December 2020, with professionals from the nursing team who worked in health care during the COVID-19 pandemic. Participants were recruited via social media, and information was collected electronically and stored on the Survey Monkey platform. Descriptive statistics were used to characterize the sample, association tests (Chi-square), with a statistical significance level of p < 0.05. Factors associated with skin lesions resulting from the use of an N95 mask were determined by Binary Logistic Regression, with a significance level of 5% (α = 0.05). Results: 8,405 nursing professionals participated in the study, 5,492 nurses (65.3%), 2,747 nursing technicians (32.7%), and 7,084 females (84.3%). Female nursing team professionals were 1.4 times more likely to develop skin lesions due to the use of N95 masks when compared to males (OR 1.4 [CI95% 1.22 – 1.59] p < 0.001). The following protective factors were identified: nursing technician (ORA 0.608 [CI95% 0.43 – 0.86] p = 0.005) and not having provided assistance in field hospitals for COVID-19 (0.73 [CI95% 0.65-0.81] p<0.000). Conclusion: It was concluded that female nursing team professionals were more likely to have skin changes related to the use of N95 masks. The need for intervention studies is emphasized in order to explore measures to prevent these types of injuries. Descritores: Nursing professionals; COVID-19; SARS-CoV-2; Brazil.

Keywords: nursing professionals, COVID-19, SARS-CoV-2, Brazil

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Authors: Kristina Sakalauskiene, Renaldas Raisutis, Gintare Linkeviciute, Skaidra Valiukeviciene

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Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth.

Keywords: cutaneous melanoma, differential diagnosis, high-frequency ultrasound, melanocytic skin tumours, spectrophotometric imaging

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Authors: Cristina Hotoleanu

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Introduction: The recent Covid-19 pandemic accelerated the development of e-health, including telemedicine, smartphone applications, and medical wearable devices. Providing remote teleconsultations supposes challenges which may require further face-to-face medical interactions. The aim of this study was to assess the correlation between the types of teleconsultations and the need of onsite medical services (investigations and medical visits) for the diagnosis and treatment. Methods: a retrospective study including all the teleconsultations using the platform offered by a telehealth provider in Romania (Telios Care SA) between May 1, 2021- April 30, 2022, was performed. Binary data were analysed using the chi-square test with a significance level of p < 0.05. Results: out of 7163 consultations, 3961 were phone calls, 1981 were online messages, and 1221 were video calls. Onsite medical services were indicated in 3327 (46.44%) cases; the onsite investigations or the onsite visits were recommended for 2908 patients as follows: 2326 in case of phone calls, 582 in case of online messages, none in case of video calls. Both onsite investigations and visits were indicated for 419 patients. The need for onsite additional medical services was significantly higher in the case of phone calls than in the other 2 types of teleconsultations (Chi square= 1207.06, p= 0.00001). The indication for onsite services was done mainly after teleconsultations covering medical specialties (87.34%), significantly higher than the other specialties (Chi square=914.59, p=0.00001). Teleconsultations in surgical specialties and other fields (pharmacy, dentistry, psychology, wellbeing- nutrition, fitness) resulted in 12.13%, respective less than 1%, indication for onsite investigations or visits, explained by using of video calls in most of the cases. Conclusion: a further onsite medical service was necessary in less than a half of the teleconsultations. This indication was done mainly after phone calls and teleconsultations in medical specialties. Video calls were used mostly in psychology, nutrition, and fitness teleconsultations and did not require a further onsite medical service. Other studies are necessary to assess better the types of teleconsultations and the specialties bringing the biggest benefit for the patients.

Keywords: onsite medical services, phone calls, teleconsultations, telemedicine

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Authors: Bishnu Prasad Dulal, Sushil Chandra Baral, Radheshyam Bhattarai, Meera Tandan

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Background: Non-use of contraceptives is a leading cause of unintended pregnancy. This study was done to explore the potential predictors of contraceptive used by young women, and the findings can inform policy makers to design the program to reduce unintended pregnancy for younger women who have a longer time of fecundity. Methodology: A nationally representative cross-sectional household survey was conducted by Health Research and Social Development Forum in 2012. Total 2259 currently married young women (15-24 years) were selected for the analysis out of 8578 women of reproductive age interviewed from the total 10260 households using systematic sampling. Binary logistic regression was used to identify factors associated with the use of modern contraceptive methods. Findings: The prevalence of modern contraceptive methods among young women was 25.2 %. Use of contraceptives was significantly associated with age at first marriage <15 year of age (OR:1.95) and ever delivered (OR: 1.8). Muslim women were significantly less likely to use contraceptives. Development region, wealth quintile, and awareness of abortion site were also statistically associated factors to use of contraceptives. Conclusion: The prevalence of contraceptives uses among young married women (25.2%) was lower than national prevalence (43%) of contraceptives use among married women of reproductive age. Our analysis focused on examining the association between women’s characteristics-related factors and use and nonuse of modern contraceptives. Awareness of safe abortion site is significantly associated while level of education was not. It is an interesting finding but difficult to interpret which needs further analysis on the basis of education. Maybe due to the underlying socio-religious practice of Muslim people, they had lower use of contraceptives. Programmers and policy makers could better help young women by increasing intervention activities to have a regular use of contraceptive-covering poor, Dalit and Muslim, and low aged women in order to reduce unintended pregnancy.

Keywords: unintended pregnancy, contraceptive, young women, Nepal

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Vishal V. R. Nandigana, Mohammad Heiranian, Narayana R. Aluru

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Graphene material has found tremendous applications in water desalination, DNA sequencing and energy storage. Multiple nanopores are etched to create opening for water desalination and energy storage applications. The nanopores created are of the order of 3-5 nm allowing multiple ions to transport through the pore. In this paper, we present for the first time, molecular dynamics study of single ion transport, where only one ion passes through the graphene nanopore. The diameter of the graphene nanopore is of the same order as the hydration layers formed around each ion. Analogous to single electron transport resulting from ionic transport is observed for the first time. The current-voltage characteristics of such a device are similar to single electron transport in quantum dots. The current is blocked until a critical voltage, as the ions are trapped inside a hydration shell. The trapped ions have a high energy barrier compared to the applied input electrical voltage, preventing the ion to break free from the hydration shell. This region is called “Coulomb blockade region”. In this region, we observe zero transport of ions inside the nanopore. However, when the electrical voltage is beyond the critical voltage, the ion has sufficient energy to break free from the energy barrier created by the hydration shell to enter into the pore. Thus, the input voltage can control the transport of the ion inside the nanopore. The device therefore acts as a binary storage unit, storing 0 when no ion passes through the pore and storing 1 when a single ion passes through the pore. We therefore postulate that the device can be used for fluidic computing applications in chemistry and biology, mimicking a computer. Furthermore, the trapped ion stores a finite charge in the Coulomb blockade region; hence the device also acts a super capacitor.

Keywords: graphene nanomembrane, single ion transport, Coulomb blockade, nanofluidics

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Authors: Kwan-Ho Kim, Jin-Hong Park

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The multi-valued logic (MVL) circuits, which can handle more than two logic states, are one of the promising solutions to overcome the bit density limitations of conventional binary logic systems. Recently, tunneling devices such as Esaki diode and resonant tunneling diode (RTD) have been extensively explored to construct the MVL circuits. These tunneling devices present a negative differential resistance (NDR) phenomenon in which a current decreases as a voltage increases in a specific applied voltage region. Due to this non-monotonic current behavior, the tunneling devices have more than two threshold voltages, consequently enabling construction of MVL circuits. Recently, the emergence of two dimensional (2D) van der Waals (vdW) crystals has opened up the possibility to fabricate such tunneling devices easily. Owing to the defect-free surface of the 2D crystals, a very abrupt junction interface could be formed through a simple stacking process, which subsequently allowed the implementation of a high-performance tunneling device. Here, we report a vdW heterostructure based tunneling device with multiple threshold voltages, which was fabricated with black phosphorus (BP) and hafnium diselenide (HfSe₂). First, we exfoliated BP on the SiO₂ substrate and then transferred HfSe₂ on BP using dry transfer method. The BP and HfSe₂ form type-Ⅲ heterojunction so that the highly doped n+/p+ interface can be easily implemented without additional electrical or chemical doping process. Owing to high natural doping at the junction, record high peak to valley ratio (PVCR) of 16 was observed to the best our knowledge in 2D materials based NDR device. Furthermore, based on this, we first demonstrate the feasibility of the ternary latch by connecting two multi-threshold voltage devices in series.

Keywords: two dimensional van der Waals crystal, multi-valued logic, negative differential resistnace, tunneling device

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Authors: Mario Alejandro Grisales, M. Daniela Chimá, Gilberto Bejarano Gaitán

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High entropy alloys (HEA) and nitride (HEN) are currently very attractive to the automotive, aerospace, metalworking and materials forming manufacturing industry, among others, for exhibiting higher mechanical properties, wear resistance, and thermal stability than binary and ternary alloys. In this work medium-entropy coatings of TiTaZrNb and the nitrides of (TiTaZrNb)Nx were synthesized on to AISI 420 and M2 steel samples by the direct current magnetron sputtering technique. The influence of the bias voltage supplied to the substrate on the microstructure, chemical- and phase composition of the matrix coating was evaluated, and the effect of nitrogen flow on the microstructural, mechanical and tribological properties of the corresponding nitrides was studied. A change in the crystalline structure from BCC for TiTaZrNb coatings to FCC for (TiTaZrNb)Nx was observed, that is associated with the incorporation of nitrogen into the matrix and the consequent formation of a solid solution of (TiTaZrNb)Nx. An increase in hardness and residual stresses was observed with increasing bias voltage for TiTaZrNb, reaching 12.8 GPa for the coating deposited with a bias of -130V. In the case of (TiTaZrNb)Nx nitride, a greater hardness of 23 GPa is achieved for the coating deposited with a N2 flow of 12 sccm, which slightly drops to 21.7 GPa for that deposited with N2 flow of 15 sccm. The slight reduction in hardness could be associated with the precipitation of the TiN and ZrN phases that are formed at higher nitrogen flows. The specific wear rate of the deposited coatings ranged between 0.5xexp13 and 0.6xexp13 N/m2. The steel substrate exhibited an average hardness of 2.0 GPa and a specific wear rate of 203.2exp13 N/m2. Both the hardness and the specific wear rate of the synthesized nitride coatings were higher than that of the steel substrate, showing a protective effect of the steel against wear.

Keywords: medium entropy coatings, hard coatings, magnetron sputtering, tribology, wear resistance

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Authors: Kristin Lewis, Howard Steele, Anne Murphy, Miriam Steele, Karen Bonuck, Paul Meissner

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The present study examines data collected during the randomized control trial (RCT) of the Group Attachment-Based Intervention (GABI©), a trauma-informed, attachment-based intervention aimed at promoting healthy parent-child relationships that support child development. Families received treatment at Treatment Center and were randomly assigned to either the GABI condition or the treatment as usual condition, a parenting class called Systematic Training for Effective Parenting (STEP). Significant improvements in the parent-child relationship have been reported for families participating in GABI, but not in the STEP control group relying on Coding Interactive Behavior (CIB) as applied to 5-minute video-films of mothers and their toddlers in a free play context. This report considers five additional attachment-relevant 'clinical codes' that were also applied to the 5-minute free play sessions. Seventy-two parent-child dyads (38 in GABI and 34 in STEP) were compared to one another at intake and end-of-treatment, on these five-point dimensions: two-parent codes—the dissociation and ignoring; two child codes—simultaneous display of contradictory behavior and fear; and one parent-child code, i.e., role reversal. Overall, scores were low for these clinical codes; thus, a binary measure was computed contrasting no evidence with some evidence of each clinical code. Crosstab analyses indicate that child fear at end-of-treatment was significantly lower among children who participated in GABI (7% or 3 children) as compared to those whose mothers participated in STEP (29% or 10 children) Chi Sq= 6.57 (1), p < .01. Discussion focuses on the potential for GABI to reduce childhood fearfulness and so enhance the child's health.

Keywords: coding interactive behavior, clinical codes, group attachment based intervention, GABI, attachment, fear

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Yibeltal T. Bayou, Yohana S. Mashalla, Gloria Thupayagale-Tshweneagae

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Background: According to the World Health Organization, caesarean section delivery rates of more than 10-15% caesarean section deliveries in any specific geographic region in the world are not justifiable. The aim of the study was to describe the level and analyse determinants of caesarean section delivery in Addis Ababa. Methods: Data was collected in Addis Ababa using a structured questionnaire administered to 901 women aged 15-49 years through a stratified two-stage cluster sampling technique. Binary logistic regression model was employed to identify predictors of caesarean section delivery. Results: Among the 835 women who delivered their last birth at healthcare facilities, 19.2% of them gave birth by caesarean section. About 9.0% of the caesarean section births were due to mother’s request or service provider’s influence without any medical indication. The caesarean section delivery rate was much higher than the recommended rate particularly among the non-slum residents (27.2%); clients of private healthcare facilities (41.1%); currently married women (20.6%); women with secondary (22.2%) and tertiary (33.6%) level of education; and women belonging to the highest wealth quintile household (28.2%). The majority (65.8%) of the caesarean section clients were not informed about the consequences of caesarean section delivery by service providers. The logistic regression model shows that older age (30-49), secondary and above education, non-slum residence, high-risk pregnancy and receiving adequate antenatal care were significantly positively associated with caesarean section delivery. Conclusion: Despite the unreserved effort towards achieving MDG 5 through safe skilled delivery assistance among others, the high caesarean section rate beyond the recommend limit, and the finding that caesarean sections done without medical indications were also alarming. The government and city administration should take appropriate measures before the problems become setbacks in healthcare provision. Further investigations should focus on the effect of caesarean section delivery on maternal and child health outcomes in the study area.

Keywords: Addis Ababa, caesarean section, mode of delivery, slum residence

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Authors: Wenbo Yu, Zihao Yuan, Zhipeng Guo, Shoumei Xiong

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Under different slow shot speeds in vacuum assisted high pressure die casting (VHPDC) process, plate-shaped specimens of hypereutectic A390 aluminum alloy were produced. According to the results, the vacuum pressure inside the die cavity increased linearly with the increasing slow shot speed at the beginning of mold filling. Meanwhile, it was found that the tensile properties of vacuum die castings were deteriorated by the porosity content. In addition, the average primary Si size varies between 14µm to 23µm, which has a binary functional relationship with the slow shot speeds. Due to the vacuum effect, the castings were treated by T6 heat treatment. After heat treatment, microstructural morphologies revealed that needle-shaped and thin-flaked eutectic Si particles became rounded while Al2Cu dissolved into α-Al matrix. For the as-received sample in-situ tensile test, microcracks firstly initiate at the primary Si particles and propagated along Al matrix with a transgranular fracture mode. In contrast, for the treated sample, the crack initiated at the Al2Cu particles and propagated along Al grain boundaries with an intergranular fracture mode. In-situ three bending test, microcracks firstly formed in the primary Si particles for both samples. Subsequently, the cracks between primary Si linked along Al grain boundaries in as received sample. In contrast, the cracks in primary Si linked through the solid lines in Al matrix. Furthermore, the fractography revealed that the fracture mechanism has evolved from brittle transgranular fracture to a fracture mode with many dimples after heat treatment.

Keywords: A390 aluminum, vacuum assisted high pressure die casting, heat treatment, mechanical properties

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Authors: Sonia Anand Knowlton

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With the rise of artificial intelligence, computer science is becoming increasingly integrated in virtually every area of life. Of course, the law is no exception. Through argumentation frameworks (AFs), computer scientists have used abstract algebra to structure the legal reasoning process in a way that allows conclusions to be drawn from a formalized system of arguments. In AFs, arguments compete against each other for logical success and are related to one another through the binary operation of the attack. The prevailing arguments make up the preferred extension of the given argumentation framework, telling us what set of arguments must be accepted from a logical standpoint. There have been several developments of AFs since its original conception in the early 90’s in efforts to make them more aligned with the human reasoning process. Generally, these developments have sought to add nuance to the factors that influence the logical success of competing arguments (e.g., giving an argument more logical strength based on the underlying value it promotes). The most cogent development was that of the Extended Argumentation Framework (EAF), in which attacks can themselves be attacked by other arguments, and the promotion of different competing values can be formalized within the system. This article applies the logical structure of EAFs to current theoretical understandings of judicial reasoning to contribute to theories of judging and to the evolution of AFs simultaneously. The argument is that the main limitation of EAFs, when applied to judicial reasoning, is that they require judges to themselves assign values to different arguments and then lexically order these values to determine the given framework’s preferred extension. Drawing on John Rawls’ Theory of Justice, the examination that follows is whether values are lexical and commensurable to this extent. The analysis that follows then suggests a potential extension of the EAF system with an approach that formalizes different “planes of attack” for competing arguments that promote lexically ordered values. This article concludes with a summary of how these insights contribute to theories of judging and of legal reasoning more broadly, specifically in indeterminate cases where judges must turn to value-based approaches.

Keywords: computer science, mathematics, law, legal theory, judging

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Authors: Manon Giraud, Francois-Damien Delapierre, Guenaelle Jasmin-Lebras, Cecile Feraudet-Tarisse, Stephanie Simon, Claude Fermon

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Early diagnosis or detection of harmful substances at low level is a growing field of high interest. The ideal test should be cheap, easy to use, quick, reliable, specific, and with very low detection limit. Combining the high specificity of antibodies-functionalized magnetic beads used to immune-capture biologic objects and the high sensitivity of a GMR-based sensors, it is possible to even detect these biologic objects one by one, such as a cancerous cell, a bacteria or a disease biomarker. The simplicity of the detection process makes its use possible even for untrained staff. Giant Magneto Resistance (GMR) is a recently discovered effect consisting in the electrical resistance modification of some conductive layers when exposed to a magnetic field. This effect allows the detection of very low variations of magnetic field (typically a few tens of nanoTesla). Magnetic nanobeads coated with antibodies targeting the analytes are mixed with a biological sample (blood, saliva) and incubated for 45 min. Then the mixture is injected in a very simple microfluidic chip and circulates above a GMR sensor that detects changes in the surrounding magnetic field. Magnetic particles do not create a field sufficient to be detected. Therefore, only the biological objects surrounded by several antibodies-functionalized magnetic beads (that have been captured by the complementary antigens) are detected when they move above the sensor. Proof of concept has been carried out on NS1 mouse cancerous cells diluted in PBS which have been bonded to magnetic 200nm particles. Signals were detected in cells-containing samples while none were recorded for negative controls. Binary response was hence assessed for this first biological model. The precise quantification of the analytes and its detection in highly diluted solution is the step now in progress.

Keywords: early diagnosis, giant magnetoresistance, lab-on-a-chip, submicron particle

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Authors: Emily Kambalame

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Water distribution networks necessitate many investments for construction, prompting researchers to seek cost reduction and efficient design solutions. Optimization techniques are employed in this regard to address these challenges. In this context, the penalty-free multi-objective evolutionary algorithm (PFMOEA) coupled with pressure-dependent analysis (PDA) was utilized to develop a multi-objective evolutionary search for the optimization of water distribution systems (WDSs). The aim of this research was to find out if the computational efficiency of the PFMOEA for WDS optimization could be enhanced. This was done by applying real coding representation and retaining different percentages of feasible and infeasible solutions close to the Pareto front in the elitism step of the optimization. Two benchmark network problems, namely the Two-looped and Hanoi networks, were utilized in the study. A comparative analysis was then conducted to assess the performance of the real-coded PFMOEA in relation to other approaches described in the literature. The algorithm demonstrated competitive performance for the two benchmark networks by implementing real coding. The real-coded PFMOEA achieved the novel best-known solutions ($419,000 and $6.081 million) and a zero-pressure deficit for the two networks, requiring fewer function evaluations than the binary-coded PFMOEA. In previous PFMOEA studies, elitism applied a default retention of 30% of the least cost-feasible solutions while excluding all infeasible solutions. It was found in this study that by replacing 10% and 15% of the feasible solutions with infeasible ones that are close to the Pareto front with minimal pressure deficit violations, the computational efficiency of the PFMOEA was significantly enhanced. The configuration of 15% feasible and 15% infeasible solutions outperformed other retention allocations by identifying the optimal solution with the fewest function evaluation

Keywords: design optimization, multi-objective evolutionary, penalty-free, water distribution systems

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Hare Krishna Doley

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Drawing upon empirical data, this paper tries to examine how ethnic groups like Ahom, Moran, Motok, and Chutia creates and recreates ethnic boundaries while making claims for recognition as Scheduled Tribes (STs) under the Sixth Schedule of the Constitution of India, in the state of Assam. Underlying such claim is the distinct identity consciousness amongst these groups as they assert themselves originally as tribe drawing upon primordial elements. For them, tribal identity promises social justice and give credence to their claims of indigeneity while preserving their exclusivity within the multifarious society of Assam. Having complex inter-group relationships, these groups under study displays distinct as well as overlapping identities, which demonstrate fluidity of identities across groups while making claims for recognition. In this process, the binary of ‘us’ and ‘them’ are often constructed amongst these groups, which are in turn difficult to grasp as they share common historical linkages. This paper attempts to grapple with such complex relationships the studied groups and their assertion as distinct cultural entities while making ethnic boundaries on the basis of socio-cultural identities. Such claims also involve frequent negotiation with the Sate as well as with other ethnic groups, which further creates strife among indigenous groups for tribal identity. The paper argues that identity consciousnesses amongst groups have persisted since the introduction of resource distribution on ethnic lines; therefore, issues of exclusive ethnic identity in the state of Assam can be contextualised within the colonial and post-colonial politics of redrawing ethnic and spatial boundaries. Narrative of the ethnic leaders who are in the forefront of struggle for ST status revealed that it is not merely to secure preferential treatment, but it also encompasses entitlement to land and their socio-cultural identity as aboriginal. While noting the genesis of struggle by the ethnic associations for ST status, this paper will also delineate the interactions among ethnic groups and how the identity of tribe is being performed by them to be included in the official categories of ST.

Keywords: ethnic, identity, sixth schedule, tribe

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Authors: M. L. Anitha, K. A. Radhakrishna Rao

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With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

Keywords: biometrics, hand geometry features, inner knuckle print, recognition

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Authors: Varun A Baheti, Sanjay Kashyap, Kamanio Chattopadhyay, Praveen Kumar, Aloke Paul

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Ni–Sn system finds applications in the microelectronics industry, especially with respect to flip–chip or direct chip, attach technology. Here the region of interest is under bump metallization (UBM), and solder bump (Sn) interface due to the formation of brittle intermetallic phases there. Understanding the growth of these phases at UBM/Sn interface is important, as in many cases it controls the electro–mechanical properties of the product. Cu and Ni are the commonly used UBM materials. Cu is used for good bonding because of fast reaction with solder and Ni often acts as a diffusion barrier layer due to its inherently slower reaction kinetics with Sn–based solders. Investigation on the growth kinetics of phases in Ni–Sn system is reported in this study. Just for simplicity, Sn being major solder constituent is chosen. Ni–Sn electroplated diffusion couples are prepared by electroplating pure Sn on Ni substrate. Bulk diffusion couples prepared by the conventional method are also studied along with Ni–Sn electroplated diffusion couples. Diffusion couples are annealed for 25–1000 h at 50–215°C to study the phase evolutions and growth kinetics of various phases. The interdiffusion zone was analysed using field emission gun equipped scanning electron microscope (FE–SEM) for imaging. Indexing of selected area diffraction (SAD) patterns obtained from transmission electron microscope (TEM) and composition measurements done in electron probe micro−analyser (FE–EPMA) confirms the presence of various product phases grown across the interdiffusion zone. Time-dependent experiments indicate diffusion controlled growth of the product phase. The estimated activation energy in the temperature range 125–215°C for parabolic growth constants (and hence integrated interdiffusion coefficients) of the Ni₃Sn₄ phase shed light on the growth mechanism of the phase; whether its grain boundary controlled or lattice controlled diffusion. The location of the Kirkendall marker plane indicates that the Ni₃Sn₄ phase grows mainly by diffusion of Sn in the binary Ni–Sn system.

Keywords: diffusion, equilibrium phase, metastable phase, the Ni-Sn system

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Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Anuja Singh, Avik K. Pati, Ashok K. Mishra

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Organic fluorophores with π-conjugated scaffolds are important because of their interesting optoelectronic properties. In recent years, our lab has been engaged in understanding the photophysics of small diacetylene bridged fluorophores and found the diynes as a promising class of π-conjugated fluorophores. Building on this understanding, recently we have focused on the photophysics of a less explored class of cross-conjugated Y-shaped enediynes (one double and two triple bonds). Here we present the photophysical properties of such enediynes which show interesting photophysical properties that include dual emissions from locally excited (LE) and intramolecular charge transfer (ICT) states and ring size dependent aggregate fluorescence in non-aqueous media. The dyes also show prominent aggregate fluorescence in mixed-aqueous solvents and solid powder form. We further show that the solid state fluorescence can be reversibly switched multiple of cycles by external stimuli, highlighting their potential applications in solid states. The enediynes with push-pull electronic substituents/moieties exhibit high contrast fluorescence color switching upon continuous photon illumination. The intriguing photophysical outcomes of the enediynyl fluorophores are judiciously exploited to generate single-component white light emission in binary solvent mixtures and sense polar aprotic vapor in polymer film matrices. The photophysical behavior of the dyes is further successfully utilized to monitor the microenvironment changes of biologically relevant anisotropic media such as bile salts. In summary, the newly introduced cross-conjugated enediynes enrich the toolbox of organic fluorophores and vouch to display versatile applications.

Keywords: aggregation in solution and solid state, enediynes, physical photochemistry and photophysics, vapor sensing and white light emission

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Authors: Ugur Choban, Aruzhan Zhaksylyk, Assylbek Nurgabdeshov

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In recent years, there has been an increasing understanding of the value of encouraging entrepreneurial attitudes in university students. This is motivated by the belief that stimulating entrepreneurship not only promotes economic growth but also fosters innovation. This study looks at the complex link and addresses critical gaps between psychological capital and entrepreneurial intention among university students, with a specific emphasis on how contextual factors like academic support and past business experience impact this dynamic. Using a quantitative research method, data were gathered from a broad sample of 300 university students drawn from several faculties. The study used a questionnaire that included the Psychological Capital Questionnaire (PCQ) to assess psychological capital and a validated scale for entrepreneurial intention, as well as binary measures of academic support and prior entrepreneurial experience. Statistical investigations, including multigroup analyses performed with SmartPLS software, provided interesting insights into the effect of contextual factors on the relationship between psychological capital and entrepreneurial intention. The findings highlight that psychological capital had a strong favorable influence on university students' entrepreneurial inclinations. Furthermore, the study found that academic support enhances the influence of psychological capital on entrepreneurial intentions, emphasizing the significance of institutional backing in fostering entrepreneurial mindsets. Furthermore, students with prior entrepreneurial experience had a stronger propensity for entrepreneurship, showing a synergistic link between psychological capital and entrepreneurial background. These findings have both theoretical and practical implications. By explaining the mechanisms by which psychological capital promotes entrepreneurial intentions, the study contributes to the establishment of focused entrepreneurship education programs and support activities that are suited to student requirements. Policymakers may use these findings to create policies that encourage student entrepreneurship, ultimately encouraging economic development and innovation.

Keywords: academic support, entrepreneurial intentions, higher education institutions, psychological capital, prior entrepreneurial experience

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Authors: Getenet Shumet Birhan, Biruk Lelisa Eticha, Gizachew Tilahun Belete, Fisseha Admassu Ayele

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Context: Glaucoma is a significant public health concern globally, being the second leading cause of blindness. This study focuses on adult glaucoma patients in Ethiopia, specifically at the University of Gondar. Research Aim: The main objective is to assess the prevalence of visual impairment and identify associated factors among adult glaucoma patients at the University of Gondar. Methodology: The study used an institution-based cross-sectional design, collecting data from 423 glaucoma patients through interviews and medical chart reviews. Descriptive statistics and logistic regression were employed for analysis. Findings: The study found a high prevalence of visual impairment (77.6%) among adult glaucoma patients, with factors such as female sex, rural residence, glaucoma type, disease stage, and duration of diagnosis significantly associated with visual impairment. Theoretical Importance: This research adds valuable insights into the prevalence and determinants of visual impairment among glaucoma patients in Ethiopia, contributing to the existing literature on eye health in low-resource settings. Data Collection: Data were collected through face-to-face interviews and medical chart reviews at the University of Gondar, utilizing a structured questionnaire. Analysis Procedures: Descriptive statistics, frequency analysis, and binary logistic regression were employed to analyze the data and identify factors associated with visual impairment in adult glaucoma patients. Question Addressed: The study sought to answer the question of the prevalence of visual impairment and its associated factors among adult glaucoma patients at the University of Gondar in Northwest Ethiopia. Conclusion: The research concludes that visual impairment is significantly high among adult glaucoma patients in this setting, with several factors playing a role in its occurrence.

Keywords: visual impairment, glaucoma, Ethiopia, Gondar

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Authors: Misganaw Chere Siferih

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This study assessed the impact of crossbreed dairy cows on household income and food security. The study area is found in Dejen Woreda, East Gojam Zone, and Amhara region of Ethiopia. Random sampling technique was used to obtain a sample of 80 crossbreed dairy cow owners and 176 indigenous dairy cow owners. The study employed food consumption score analytical framework to measure food security status of the household. No Statistical significant mean difference is found between crossbreed owners and indigenous owners. Logistic regression was employed to investigate crossbreed dairy cow adoption determinants , the result indicates that gender, education, labor number, land size cultivated, dairy cooperatives membership, net income and food security status of the household are statistically significant independent variables, which explained the binary dependent variable, crossbreed dairy cow adoption. Propensity score matching (PSM) was employed to analyze the impact of crossbreed dairy cow owners on farmers’ income and food security. The average net income of crossbreed dairy cow owners was found to be significantly higher than indigenous dairy cow owners. Estimates of average treatment effect of the treated (ATT) indicated that crossbreed dairy cow is able to impact households’ net income by 42%, 38.5%, 30.8% and 44.5% higher in kernel, radius, nearest neighborhood and stratification matching algorithms respectively as compared to indigenous dairy cow owners. However, estimates of average treatment of the treated (ATT) suggest that being an owner of crossbreed dairy cow is not able to affect food security significantly. Thus, crossbreed dairy cow enables farmers to increase income but not their food security in the study area. Finally, the study recommended establishing dairy cooperatives and advice farmers to become a member of them, attention to promoting the impact of crossbreed dairy cows and promotion of nutrition focus projects.

Keywords: crossbreed dairy cow, net income, food security, propensity score matching

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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