bluuebunny/crossref_metadata_2025 huggingface dataset:
Network Intrusion Detection Method Using Stacked BILSTM Elastic Regression Classifier with Aquila Optimizer Algorithm for Internet of Things (IoT)
Authors: Gangula Rekha , Vutukuru Murali Mohan , Kumar M. Ranjeeth
Published: April 2023

Globally, over the past ten years, computer networks and Internet of Things (IoT) networks have grown significantly due to the increasing amount of data that has been collected, ranging from zettabytes to petabytes. As a result, as the network has expanded, security problems have also emerged. The large data sets involved in these types of attacks can make detection difficult. The developing networks are being used for a multitude of sophisticated purposes, such as smart homes, cities, grids, gadgets, and objects, as well as e-commerce, e-banking, and e-government. As a result of the development of numerous intrusion detection systems (IDS), computer networks are now protected from security and privacy threats. Data confidentiality, integrity, and availability will suffer if IDS prevention efforts fail. Complex attacks can't be handled by traditional methods.  There has been a growing interest in advanced deep learning techniques for detecting intrusions and identifying abnormal behavior in networks. This research aims to propose a novel network namely stacked BiLSTM elastic regression classifier (Stack_BiLSTM-ERC) with Aquila optimizer algorithm for feature selection. This optimization method computes use of a cutting-edge transition function that enables it to be transformed into a binary form of the Aquila optimizer. A better solution could be secured once number of possible solutions are found from diverse regions of the search space utilizing the Aquila optimizer method. NSL-KDD and UNSW-NB15 are two datasets that enable learning characteristics from the raw data in order to detect harmful prerequisites characteristics and effective framework patterns. The proposed Stack_BiLSTM-ERC achieves 98.l3% of accuracy, 95.1% of precision, 94.3% of recall and 95.4 of F1-score for NSL-KDD dataset. Moreover, 98.6% of accuracy, 97.2% of precision, 98.5 of recall and 97.5% of F1-score.
Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data
Authors: Kulkarni Amogh Pramod , T. N. Manjunath
Published: April 2023

Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works.
An Energy Efficient and Cost Reduction based Hybridization Scheme for Mobile Ad-hoc Networks (MANET) over the Internet of Things (IoT)
Authors: Jhade Srinivas , Kumar V. Senthil , Kuntavai T. , Shekhar Pandey Purnendu , Sundaram Ajith , Parasa Gayatri
Published: April 2023

Wireless networks are viewed as the best-used network and specifically Portable Specially Appointed Organizations (MANETs) have tracked down numerous applications for its information transmission progressively. The plan issues in this organization are to confine the utilization of energy while communicating data and give security to the hubs. Soa protocol needs to be energy efficient to avoid network failures. Thereby this paper brings an effective energy efficient to optimize LEAR and make it energy efficient. The energy-mindfulness element is added to the LEAR guiding convention in this work using the Binary Particle Swarm Optimization method (BPSO). The recommended method selects programmes taking into account course length in addition to the programme level of energy when predicting the future. To get good results, the steered challenge is first designed using LEAR. The next step is to choose a route that enhances the weighting capability of the study hours and programming power used.This MANET has been secured using the cryptographic method known as AES.According to experimental findings, the proposed hybrid version outperformed other cutting-edge models.
EECLA: A Novel Clustering Model for Improvement of Localization and Energy Efficient Routing Protocols in Vehicle Tracking Using Wireless Sensor Networks
Authors: Gummadi Annapurna , Rao K Raghava
Published: April 2023

Due to increase of usage of wireless sensor networks (WSN) for various purposes leads to a required technology in the present world. Many applications are running with the concepts of WSN now, among that vehicle tracking is one which became prominent in security purposes. In our previous works we proposed an algorithm called EECAL (Energy Efficient Clustering Algorithm and Localization) to improve accuracy and performed well. But are not focused more on continuous tracking of a vehicle in better aspects. In this paper we proposed and refined the same algorithm as per the requirement. Detection and tracking of a vehicle when they are in larges areas is an issue. We mainly focused on proximity graphs and spatial interpolation techniques for getting exact boundaries. Other aspect of our work is to reduce consumption of energy which increases the life time of the network. Performance of system when in active state is another issue can be fixed by setting of peer nodes in communication. We made an attempt to compare our results with the existed works and felt much better our work. For handling localization, we used genetic algorithm which handled good of residual energy, fitness of the network in various aspects. At end we performed a simulation task that proved proposed algorithms performed well and experimental analysis gave us faith by getting less localization error factor.
An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model
Authors: Gandhi Lalit , Rishi Rahul , Sharma Sonia
Published: April 2023

This paper proposes a novel, efficient and robust tuple time stamped hybrid historical relational model for dealing with temporal data. The primary goal of developing this model is to make it easier to manage historical data robustly with minimal space requirements and retrieve it more quickly and efficiently. The model's efficiency and results were revealed when it was applied to an employee database. The proposed model's performance in terms of query execution time and space requirements is compared to a single relational data model. The obtained results show that the proposed model is approximately 20% faster than the conventional single relational data model. Memory consumption results also show that the proposed model's memory cost at different frequencies is significantly reduced, which is approximately 30% less than the single relational data model for a set of queries. Because net cost is strongly related to query execution time and memory cost, the suggested model's net cost is also significantly reduced. The proposed tuple timestamp hybrid historical model acts as generic, accurate and robust model. It provides the same functionality as previous versions, as well as hybrid functionality of previously proposed models, with a significant improvement in query execution speed and memory usage. This model is effective and reliable for the use in a wide range of temporal database fields, including insurance, geographic information systems, stocks and finance (e.g. Finacle in Banking), data warehousing, scientific databases, legal case histories, and medical records.
Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis
Authors: Kakulapati V. , Jayanthiladevi A.
Published: April 2023

In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods.
Performance of Deep Learning in Land Use Land Cover Classification of Indian Remote Sensing (IRS) LISS – III Multispectral Data
Authors: Desai Nirav , Shukla Parag
Published: April 2023

Identification of land use land cover is a very important task. However, methods existing for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays very important role in the mappings. classification of land cover features and offers very noteworthy and sensed information. The present study shows the semantic segmentation of Indian remote sensing (IRS) LISS-III multispectral image and the comparison of three algorithms U-Net, Deeplabv3+and Tiramisu. The deep neural network was used to perform the study. We present total 3 innovative datasets, built on these LISS-III images that has 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), FCC (false color composite) images and the ground truth mask images. Dataset has 13500 labelled images. A fully-convolutional network (FCN) with skip connections is trained to take an input image of size 128 X 128 X 3 and outputs a matrix of shape 128 X 128 X 4 i.e., a one-hot encoded version of the mask. The experiment identifies 4 classes successfully (Water Bodies, Vegetation, Uncultivated Land, and Residential areas). The experiment showed that the U-Net algorithm has a very good capability for the classification of LISS -III images for land use land cover class detection then Tiramisu and Deeplabv3+. U-Net achieved accuracy 84%, Deelabv3+ achieved 29% whereas Tiramisu achieved accuracy 33%.
MLO Mammogram Pectoral Masking with Ensemble of MSER and Slope Edge Detection and Extensive Pre-Processing
Authors: Ch.Sarada Ch.Sarada , Lakshmi K. Vijaya , Padmavathamma M.
Published: April 2023

Breast Cancer is a fatal disease. Several people are losing their lives as a result of Breast Cancer. Mammography is the most often used Breast screening modality where we can see both mass and microcalcifications and both are the two major indicators of Breast Cancer. We can see Pectoral muscle also on MLO Mammograms. Digital Image Processing based computer aided diagnosis systems are being used widely to help the radiologist in detecting mass and microcalcifications in MLO Mammograms. However, because the intensity levels of the Pectoral muscle are similar to masses, in computer aided diagnosis system, Pectoral presence in the Mammogram has a detrimental effect on identifying mass. Therefore, in computer aided diagnosis system, Pectoral muscle masking substantially enhances lesion detection. This study suggests a novel ensemble computer aided diagnosis system strategy that combines the MSER based and SlopeEdgeDetection methods with extensive pre-processing to identify and cover Pectoral muscle from MLO Mammograms. The results demonstrate that the new procedure is straightforward and improves the precision of Pectoral region covering. Compared to the average accuracy of the state-of-the-art solutions which is 94%, the suggested technique achieves an accuracy of 99%. Performance analysis makes use of the Mini-MIAS database.
Digital Companion for Elders in Tracking Health and Intelligent Recommendation Support using Deep Learning
Authors: Tamilselvi T. , Lakshmi D. , Lavanya R. , Revathi K.
Published: April 2023

Ambient assisted living (AAL) facilitates the daily routines of elderly people, particularly those who have clinical difficulties or physical limitations. The latest technologies like distributed compuring,internet of things (IoT) and machine learning pave the ground for the creation of an effective automated tracker which aids elder citizens to live independently. The suggested system is attempted to design a wearable that monitors the blood glucose level through sweat. To achieve high accuracy, the proposed system uses ambient sensing and deep learning based techniques. It places a strong emphasis on calculating the health index by taking into account numerous disease-related characteristics or vitals such as heart rate, blood pressure, SpO2, blood glucose level, respiration rate, sweat rate, uric acid, and temperature. From the wearable device designed the vital signs are gathered, further environmental sensors and camera fixed around the person continually monitors the behavioral pattern along with physiological signals. This ensures the improved accuracy of health state prediction from its conventional models in place. The key advantage of this device is that it may be held and operated anyplace without interrupting their day-to-day tasks because the device is to be cheap, reliable and speedy.
Generic Home Automation System Using IoT Gateway Based on Wi-Fi and ant+ Sensor Network
Authors: Yadav Deepak , Gangwar Sanjeev
Published: April 2023

This research article explores the use of internet of things (IoT) technology in home automation, including cloud computing and sensor networks to improve quality of life, and the increasing affordability through mobile connectivity. In this proposed smart home system, our main objective is to build a home automation system for the common consumer, which can help him to use home appliances with confidence and control at a low cost. The paper describes the building of an IoT gateway using the ANT multi-hop wireless network protocol and the Wi-Fi protocol, specifically utilizing the nRF24L01 and Esp8266 chips. Various sensor nodes, such as a water tank level sensor, human presence sensor, smart LED door sensor, and smart switch, will be integrated into the system. The main goal of the research is to develop an affordable solution for smart home technology for everyday consumers.
Crop Yield Prediction Using Gradient Boosting Neural Network Regression Model
Authors: Naidu k Purushotam , Rao V. Lakshmana , Gunturu Chaitanya Sree , Niharika Akkina , Anupama Ch. Rohitha , Srivalli G.
Published: April 2023

The finest utility sector is agriculture, especially in emerging nations like India. Utilizing historical data in agriculture can change the context of decision-making and increase farmer productivity. Approximately a part of India's population is employed in agriculture, however this sector contributes just 14% of the country's GDP. This can be explained in part by farmers not making sufficient decisions on yield forecast. By examining numerous climatic elements, such as rainfall, and land characteristics, such as soil type and ground water salinity, as well as historical records of crops cultivated, the suggested machine learning technique tries to estimate the agricultural yield for a certain location. Finally, we anticipate that our proposed Machine Learning Gradient Boosting Neural Network Regression (Grow Net) model was predicting the accurate yield. Finally our system is expected to predict the yield based on dataset we have taken. We were compared our proposed algorithm with various Machine Learning algorithms such as Random Forest, Support Vector Machine, KNN, Multi-layer Perceptron Regressor, Gradient Boosting Regressor and results shows that proposed was given best RMSE ,MAE and R2 value.
Technology for Kisan Samanvayam: Nutrition Intelligibility of Groundnut Plant using IoT-ML Framework
Authors: Latha S.Bhargavi , Gundavarapu Mallikarjuna Rao , Kumar NVS Pavan , Parameswari D V Lalita , Reddy B Rama Krishna
Published: April 2023

Neolithic Demographic transition resulting the reduction of habitable land for cultivation. Hence the smart agriculture is the only way to cater higher food demand. The farming community of developing countries like India needs Kisan Samanvayam with futuristic technologies for financially viable cultivation. Technology place vital role in economically nourishment of soil fertility and crop management. In this regard we proposed IoT-ML framework for remotely assessing the soil nutrients (N, P,K), PH and early stage detection of crop deceases. Android APP which is a part and parcel of the frame work enable the farmer to have real time visual statistics of the soil nutrients, notifications and suggestions regarding to the crop management. JXCT Soil NPK sensors, PH sensors, Dual Core ESP32 Controllers, Firebase Cloud and Random Forest Decision Tree machine Learning Algorithm, Micromlgen serve this purpose. Unlike Solitary sensor for entire field, we have divided a hector into four subregions for effective monitoring local region needs. The presence of IoT with TinyML increased the robustness of the framework and results are encouraging with sandy loam soil.
Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique
Authors: Sikri Alisha , Singh N. P. , Dalal Surjeet
Published: April 2023

In order to improve classification accuracy and lower future computation and data collecting costs, feature selection is the process of choosing the most crucial features from a group of attributes and removing the less crucial or redundant ones. To narrow down the features that need to be analyzed, a variety of feature selection procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), and MI are six alternative feature selection methods used in this study. The provided dataset is aggregated using four rank aggregation strategies: "rank aggregation," "Borda Count (BC) methodology," "score and rank combination," and "unified feature scoring" based on the outcomes of the six feature selection method (UFS). These four procedures by themselves were unable to generate a clear selection rank for the characteristic. To produce different ranks of traits, this ensemble of aggregating ranks is carried out. For this, the bagging method of majority voting was applied.
Analysis and Classification of Breast Cancer Disease Via Different Datasets and Classifier Models
Authors: Barwal Ravi Kumar , Raheja Neeraj , Mohan B R , U Yamuna , Gadde Sai Sudha , Patil Madhu
Published: April 2023

Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system.
Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models
Authors: Mehta Ekta S , Padhi S.N.
Published: April 2023

To achieve high quality products from Plastic Injection Molding (PIM) process it is very essential to identify the defective operations in automatic manner which is most challenging task. This paper proposes a Machine Learning (ML) approach to detect the complex faults occurrence during the PIM process. During initial sampling process of molding to achieve high quality and low time consumption it is essential to concentrate on the suitable determination of parameter values by considering the properties of injection molding process. For that purpose, a novel machine learning algorithms based gating system is introduced in PIM (MLGS-PIM). Technical evaluation can be done using simulation which combines the CATIA and MATLAB. Therefore in MLGS-PIM, a holistic approach is introduced to improve and predict the process quality of the parameters which is based on machine learning approaches. The considered machine learning approaches for this process are Artificial Neural Network (ANN) and Support Vector Machine (SVM). This two learning models are combined to achieve high quality under various conditions. Such novel ML based technique helps to increase the quality characteristics of the injection molding process and it is predicted with various parameter values where the simulation data and measurements are handled in an intelligent manner. The materials which are considered in the PIM process are thermoplastic polystyrene, thermoplastic acrylonitrile butadiene styrene and thermoplastic polyvinyl chloride where three types are gating systems are applied with it and consists of 3, 4 and 5 gates and as well the parameters which are measured for the output analysis are sum rate, bit error rate and convergence plot. The results show that the performance of the proposed MLGS-PIM approach significantly increases the performance when compared with the earlier approaches such as AntLion Optimization and PSO-MSQPA.
Denoising ECG Signal Using DWT with EAVO
Authors: Balasubramanian S. , Naruka Mahaveer Singh , Tewari Gaurav
Published: April 2023

Cardiovascular diseases are the leading cause of death across the world, and traditional methods for determining cardiac health are highly invasive and expensive. Detecting CVDs early is critical for effective treatment, yet traditional detection methods lack accessibility, accuracy, and cost-effectiveness – leaving patients with little hope of taking control of their own cardiac health. Noisy ECG signals make it difficult for health practitioners to accurately read and determine heart health. Unreliable readings can lead to misdiagnosis and needless expense. Despite the importance of ECG analysis, traditional methods of signal denoising are inefficient and can produce inaccurate results. This means that medical practitioners are struggling to obtain reliable readings, leaving them unable to accurately treat their patients and leading to a lack of confidence in the medical field. The Enhanced African Vulture Optimization (AVO) algorithm with Discrete Wavelet Transform (DWT) optimized by adaptive switching mean filtration (SMF) is proven to provide accurate denoising of the ECG signal. With this reliable method, medical professionals can quickly and accurately diagnose patients. Obtaining accurate ECG signals and interpreting them quickly is a challenge for healthcare professionals. Not only it takes a lot of time and skill but also requires specialized software to interpret the signals accurately. Healthcare professionals are facing a serious challenge when it comes to obtaining accurate ECG signals and interpreting them quickly. It requires them to spend extra time and effort, as well as specialize in the field with expensive software. Time is of the essence in healthcare and ECG readings can mean the difference between life and death. Specialized software can be expensive and time-consuming for those who don't have the resources or expertise. Our easy-to-use platform allows healthcare professionals to quickly interpret ECG signals, saving time, money, and lives! Get accurate readings. The EAVO algorithm and MIT-BIH dataset provide an effective solution to this problem. With the proposed filter built using EAVO, businesses can attain significant enhancements in reliable parameters and obtain accurate testing results in terms of SNR, MD, MSE and NRMSE.
Hypertrophic cardiomyopathy: a modern view of the problem
Authors: Kalyagin Alexey N. , Gamayunov Danil Yu. , Silkin Viktor I. , Kurkov Nikolay N.
Published: April 2023

Cardiomyopathy is considered one of the main causes of heart failure and sudden cardiac death, at least in young people. Approximately 50% of patients who die suddenly in childhood or adolescence or undergo heart transplantation suffer from this condition. The purpose of this literature review is to study and highlight the issues of etiology, pathogenesis, clinical features, diagnosis and treatment of hypertrophic cardiomyopathy from the point of view of modern ideas. The search and analysis of domestic and foreign literature materials using the PubMed and eLibrary databases was carried out. Of particular interest is the etiology of primary congenital cardiomyopathies, in respect of which research continues. As a result of the implementation of genetic factors, multiple structural and functional changes in the myocardium develop, which lead to changes in hemodynamics. Cardiomyopathy is a clinically heterogeneous disease, and one of the factors that determine the clinical phenotype is the genotype. In addition to standard laboratory testing, patients with suspected hypertrophic cardiomyopathy are advised to undergo medical genetic counseling to identify the causative mutation, and often to obtain prognostic information. The fundamental imaging method is echocardiography, but the role of magnetic resonance imaging in the diagnosis of the disease is also considered. Patients with symptomatic obstructive hypertrophic cardiomyopathy are usually recommended first-line pharmacotherapy with -blockers or non-dihydropyridine calcium channel blockers. Currently, research on new drugs for the treatment of hypertrophic cardiomyopathy inhibitors of cardiac myosin is ongoing. Surgical methods of treatment are developing progressively, however, methods of conservative treatment require further active research of drugs that have not been used before.
Characteristics of Background Noise in the Oldest-1 Array Deployed on the Oldest Part of the Pacific Plate
Authors: Kim Tae-shin , Park Ji-hoon , Ko Ji-won , Oh Se-young , Witek Michael , Chang Sung-Joon , Lee Sang-Mook , Kim YoungHee , Utada Hisashi , Kawakatsu Hitoshi , Shiobara Hajime , Isse Takehi , Takeuchi Nozomu , Sugioka Hiroko
Published: April 2023

We characterize the sources of background noise recorded by an ocean-bottom seismometer array deployed on the oldest oceanic lithosphere of the western Pacific plate. This igneous oceanic crust is blanketed up to 1000 m cover of sediments, making it a unique place to investigate correlations between sediment thickness and background noise levels in the marine environment. Although the data are limited, we found that the thicker sediments attenuated short-period noise levels and amplified long-period noise levels, in agreement with observations made for seismic arrivals in onshore sedimentary basins. Comparison of the noise levels from our Oldest-1 deployment with those (1) in the western Pacific basin, (2) near the Mariana trench, and (3) offshore New Zealand indicates that the Oldest-1 has the highest noise levels in the double-frequency (secondary) microseism band by up to 7 dB. This result was unexpected given the greater water depths (averaging 5800 m) for the Oldest-1 array. However, even at these great depths, we find a strong correlation between significant wave heights and background noise levels in the short-period microseism band (2–5 s). In addition, the Oldest-1 array was subjected to a great number of intense close typhoons than the other arrays and was proximal to a great number of taller seamounts than the other arrays. Finally, tracking noise source azimuths using station pairs led to the identification of a possible new source for the single-frequency microseism band in the coastal areas of Papua New Guinea and Australia.
Mastri: Komik Digital untuk Memotivasi Remaja dalam Optimalisasi Manajemen Waktu
Authors: Panjaitan Tondy Raja , Samodra Joko , Sutrisno Arif
Published: April 2023

Abstract: Comics are visual media best known as a popular information or entertainment medium among teenagers. Most teenagers tend to read comics in fiction, drama, and comedy genres. Many adolescents present low enthusiasm and motivation to develop their potential for the ideal bright future induces teenagers to fill their spare time with wasteful activities, especially after the emergence of not-educational entertainment media. Therefore, their time and energy are diverted from essential aspects, such as learning, self-development, and preparation for the future. This study aims to develop an application of digital comics to motivate teenagers to optimize their time management. For the research method, we used a development design consisting of pre-production, production, and post-production stages. Our analysis results suggested that this digital comic is beneficial for society, especially among teenagers, as it motivates and improves time management skills among adolescents. Besides, the results of our survey involving 40 respondents also showed that the “Mastri” comic attained a score of 88,9 percent, suggesting its positive impression and efficiency in providing education about time management from the respondents’ viewpoint.
Keywords: comics, time management, youth
Abstrak: Komik merupakan media visual yang paling dikenal sebagai media informasi atau hiburan yang popular dikalangan remaja. Komik yang biasa dibaca oleh remaja cenderung ke genre fiksi, drama dan komedi. Rendahnya semangat dan motivasi untuk mengembangkan potensi diri demi mem­persiapkan masa depan yang cerah sesuai dengan cita-cita membuat remaja cenderung mengisi waktu luang dengan kegiatan tidak bermanfaat. Salah satu faktor yang menjadi penyebabnya yaitu media hiburan yang tidak mendidik. Waktu dan tenaga remaja menjadi teralihkan dari hal-hal yang penting seperti belajar, pengembangan diri, dan persiapan masa depan. Tujuan dari penelitian ini untuk mengetahui penerapan komik digital untuk  memotivasi remaja dalam optimalisasi manaje­men waktu. Penelitian ini menggunakan metode perancangan dengan menggunakan alur tahapan seperti pra, tahap produksi, dan tahap pasca produksi. Hasil penelitian menunjukan bahwa komik digital ini bermanfaat bagi masyarakat terutama dikalangan remaja serta membuat remaja yang berada di bangku sekolah semakin termotivasi dan dapat mengelola manajemen waktu dengan baik. Setelah melakukan survey dengan 40 resonden menunjukkan bahwa  komik “Mastri” sangat baik dengan menunjukkan skala nilai 88,9 persen, sehingga dapat disimpulkan bahwa melalui komik memiliki kesan positif dan cukup efektif untuk memberikan edukasi tentang memotivasi diri untuk memanajemen waktu dari sebuah cerita yang berdasarkan mahasiswa santri tersebut.
Kata kunci: komik, manajemen waktu, remaja
Sero-Prevalence of Syphilis Among Blood Donors in Port Sudan city, Sudan, 2016
Authors: Hassan A.A. AL , Abdalmajed M.A. , Mohamadeen Tahane Bashir , Mohamadeen Nagat Bashir
Published: April 2023

Background: Several infectious diseases have been found to be associated with transfusion of blood and blood components. Studies conducted in many African countries indicate a high prevalence of blood-borne pathogens such as Syphills infections among healthy blood donors.

Objectives: This study conducted to determine the prevalence of syphills among blood donors and  associated risk factors in Port Sudan city, 2016.

Materials and methods: Across sectional laboratory-based study conducted in Port Sudan city. A Blood donor in central blood bank in Port Sudan during the period between August-October-2016. The data was collected through a structured questionnaire. Data was analyzed using SPSS version 15.0.

Results: The prevalence of syphilis among blood donors were 13.24%. The risk factors associated with prevalence syphills were significantly the age group 40-49 years, hard workers, primary schools donors and blood donors who has two sex partnerships, p<0.05.

Conclusion: The overall prevalence of syphills among blood donors in Portsudan city is 13.2% which is high in comparison to prevalence of noticed studies was done in different areas in the country. The risk factors associated with prevalence syphills were significantly the age group 40-49 years, hard workers, primary schools donors and blood donors who have two sex partnerships. Early and accurate detection of cases and conduction of effective treatment as to stop the progression of disease which can be infective to others. In addition to Health education on blood donations and safe sex behaviors targeting sexually active population, particularly between 20-50years old.
Assessment of Household Food Insecurity and Associated Factors in Rural Communities of Gondar City Administration
Authors: Melese Yirdaw
Published: April 2023

Ethiopia is currently ranked 169 out of 177 countries on the 2007- 2008 Human Development Index and is chronically suffers from food insecurity. Food shortages in Ethiopia aggravate the already poor health of children and adults. Millions of households in rural areas of Ethiopia suffer from chronic food insecurity and receive food aid on an annual basis. Objective:-This study was conducted to determine prevalence of household food insecurity and its associated factors in rural communities of Gondar City Administration, North West Ethiopia. Methods:-Community based cross-sectional study design was employed. Data were collected through house to house interview using household food insecurity access scale (HFIAS) which is structured and universally applicable household food insecurity 9 item measurement tools. Descriptive statistics and logistic regressions were used to determine prevalence of household food insecurity and its associated factors. Result: - 60.8% of the rural households were found to be food insecure in the study area. Out of the total eleven variables included in the logistic regression analysis model only 6 variables namely; livestock ownership (AOR= 2.05, 95%CI = 1.039- 4.022), use of fertilizer (AOR = 4.23, 95% CI = 1.741-10.275), source of potable water (AOR = 4.00, 95% CI = 2.060-7.764), off-farm income (A0R = 2.90, 95% CI = 1.249-6.712), annual farm income (AOR = 3.98, 95% CI = 2.514-6.303) and total annual income (AOR = 3.93, 95% CI = 2.471-6.258) were found to be statistically significant. Conclusion and recommendation: - The majority (60.8%) of households in rural communities of Gondar City Administration is food insecure and factors like livestock ownership, use of fertilizers, source of potable water, off-farm income, farm income and total annual income are found to be significantly associated with household food insecurity in the study area. Thus, Government bodies, NGOs and the community at large should work cooperatively in areas that enable rural households to minimize the problem of food insecurity by overcoming factors associated with it.
Mimo Channel and Performance Analysis using OFDM System for Reduced Bit Error Rate
Authors: Hossain Saddam , Mrida Md. Izaz Arefin , Das Pradip Chandra , Sayed Abu , Rahman Nahid , Rashid Md Mamunur
Published: April 2023

Multiple-input Multiple-output (MIMO) systems use multiple antennas at the transmitter and receiver end, are a key technology to meet the growing demand for high data rate wireless systems. The aim of this thesis is to investigate MIMO system capacity with the aim of achieving optimum Bit Error Rate (BER) while increasing the system capacity using multicarrier delay diversity modulation (MDDM), proposed for fifth generation systems. In principle, the capacity of MIMO system can increase linearly with the number of antennas. Multiple antennas at the transmitter and receiver provide diversity in a fading environment. Furthermore, the research work in this thesis consists of different investigations of the basic principle of MIMO, Multiple-input Single-output (MISO) and Single-input Single-output (SISO) wireless communication systems with Space Time Codes (STC). A MISO systems and MIMO systems are schematized using MDDM which incorporated with Orthogonal Frequency Division Multiplexing (OFDM). OFDM is chosen over a single-carrier solution due to lower complexity of equalizers for high delay spread channels or high data rates. The design is implemented with binary Phase Shift Keying (BPSK) and simulated using MATLAB, which is examined in associated Additive White Gaussian Noise (AWGN) channel. The receiver-design is included with the maximal ratio combiner (MRC) receiving technique with perfect wisdom of channel state information (CSI). The theoretical performance is derived for AWGN channels and compared with the simulated results as well as compared between each system to another.
ICTs for Improving National Competitiveness, Case Study of Iranian SMEs
Authors: Abdollahpouri Maria
Published: April 2023

There is fierce competition among firms to gain a larger customer-base. Countries, like firms, are in competition to stand out in global competitiveness and world market. Effective competition and achieving advantage over competitors are closely associated with the concept of competitive advantage. Various sources of competitive advantage are found in the literature. However, Information and Communication Technologies (ICTs) are increasingly becoming the center of attention for creating competitive advantage both at firm and country level. A large group of firms in every country are Small and Medium Enterprises (SMEs). SMEs are widely known as enablers of business and economic growth; nevertheless, they constantly face challenges in competition. In this study, ICTs are introduced as a strategy for SMEs to improve their competitiveness. It is suggested that promoting competition among SMEs could lead to a higher performance in competitiveness at country level. This is investigated by focusing on Iran as a country case study. The present research concludes by the strong potentiality of ICTs to improve Iran’s national competitiveness through making the home market more competitive for its SMEs.
Factors Affecting Tourist Satisfaction: A Study in Sylhet Region
Authors: Roy Debashish , Dhir Md. Gulam Mokta , Ahsan Mohammad Kamrul
Published: April 2023

Tourism is one of the largest growing industries around the world and it has a great role to the GDP in Bangladesh. This paper investigates the core factors that can influence satisfaction level of tourists with specific reference to tourism industry in Bangladesh. Satisfied tourists will revisit the place and they will provide positive word-of-mouth. So it is indispensable to identify which factors around the tour destinations have impact on overall satisfaction of tourists. In carrying out this research, primary data have been collected from 428 tourists of different tourism spots in Sylhet region using structured questionnaire. It is found in the study that the tourists’ satisfaction greatly depends on natural beauty, transportation and accommodation facilities, safety and security, and costs. This study reveals that tourist satisfaction depends on a complex process where the role of every actor is fundamental and those actors should be harmonized with each other. Furthermore, this study allows identifying the current strengths and weaknesses of tourism services in Sylhet which may reflect the overall scenario of Bangladesh.
The Treaty of Rome EEC and EURATOM 1957
Authors: Graziatti Lucia Vallecillo
Published: April 2023

After the crisis of the EDC, European integration focused on the economic field as the best way to accelerate the process. The leaders of the member states of the ECSC plus the UK met in 1955 in Messina in a conference where they agreed upon the creation of a preparatory committee for the construction of a common market and also a community based on atomic energy. It was a year after the rejection of the EDC, and all the parties involved were very cautious to avoid another failure that could have doomed the whole European project.
Risks of Multi-Cloud Environment: Micro Services Based Architecture and Potential Challenges
Authors: Chimakurthi Venkata Naga Satya Surendra
Published: April 2023

Over the most recent couple of years, a modified programming engineering style has been created to plan new programming applications. This engineering style is especially appropriate for use cases in the avionic business, from an autonomously deployable programming administration. These administrations are worked around business and mission capacities and are freely deployable by completely computerized apparatus. With microservices, a few sorts of uses become simpler to construct and keep up with when they are separated into more modest, composable pieces that cooperate. This is rather than a conventional, "solid" application that is completely evolved in one piece. This paper will talk about, a few parts of microservices-based engineering, including a few potential use cases for the aeronautic trade. The qualities of microservice-based engineering like componentization, association, endpoints, and informing systems. The specialized execution of microservices by auditing containerization, administrations correspondence, and related design parts. Explicit open-source tasks and parts that can be used to fabricate the microservices-based design. An example set of utilization cases.
Effect of Different Amount of Water Hyacinth as Mulch on Potato and Tomato at the Saline Soil of Noakhali
Authors: Bhowmik S. K. , Bhowal S. K. , Chowdhury M. M. , Rahman M. A. , Faisal A. H. M. A. , Farhad I. S. M.
Published: April 2023

The experiment was conducted at the farmer’s field of FSRD site, Hazirhat, Noakhali, during the Rabi season (2014-15 & 2015-16) to observe the effect of different amount of water hyacinth as mulch on potato and tomato at the saline soil of Noakhali. Different mulch treatments viz. T1 = No mulch, T2 =56 t/ha, T3 = 62 t/ha and T4 = 68 t/ha were tested in the experiment. Different amount of water hyacinth had marked effect on soil moisture conservation to reduce the adverse effect of soil salinity. The maximum soil moisture (%) status and the lowest salinity level (dS m-1) recorded both for potato and tomato at harvest stage mulched with 68 t water hyacinth ha-1. The highest yield 26.46 t ha-1 and 67.29 t ha-1coupled with gross margin 165800 tk. ha-1 and 646930 tk. ha-1 obtained from T4 treatment both for potato and tomato, respectively. Cultivation of Potato and Tomato with water hyacinth as mulch @ 68 t ha-1 might be a good option to minimize the adverse effect of soil salinity as well as for higher yield and economic return at the saline soil of Noakhali.
Question Answering System over Linked Data: A Detailed Survey
Authors: Rasiq G. M. Rasiqul Islam , Sefat Abdullah Al , Hossain Tanjila , Munna Md. Israt-E-Hasan , Jisha Jubayeath Jahan , Hoque Mohammad Moinul
Published: April 2023

As the amount of information in the world is growing very quickly, in the case of the semantic web this increasing amount of information is becoming more difficult to find and manage the exact answers to our various questions. To overcome these difficulties some systems have been developed that make it work for us. But there exists many challenges in developing these systems that require a lot of improvement. In this tutorial, we give a basic understanding of Semantic web, RDF triple, SPARQL query language. Here we will discuss the main obstacles for the QA system in processing the questions and a detailed survey of the existing systems. We also provide some advantages and disadvantages of existing QA systems. We also discuss the evaluation campaigns of the existing models based on their precision, recall and F-1 scores on the QALD dataset.
Survey on Common Practices of Vegetables Cultivation in Floating Bed at Barishal District, Bangladesh
Authors: Hossain Md. Uzzal
Published: April 2023

Floating bed vegetable cultivation is now extended to wetland areas and long term water logging areas from southern parts of Bangladesh. Vegetables and other horticultural crops are now being cultivated in this method and getting popularity day by day. This method of cultivation is now providing numerous socio-economic, ecological and agricultural benefits towards climate change to local people. To face the circumstances from changed climate system effective adaptation actions and knowledge transfer is needed. This survey was conducted to find out socio-economic aspects, floral diversity cultivated, yield performance of cultivated crops, diversification of this technology, and also to find out the problems related to cultivation of this system. It was found that both sexes participate almost equally that leads to gender equity. In addition, a diversification in construction materials, fertilizers uses, yield purposes and crops cultivated were also found.
Radiation Risk Assessment on Public in Motijheel Thana, Dhaka, Bangladesh
Authors: Hossain Mohammed Belayet , Rahman Dr. Mohammad Sohelur , Bhuiyan Dr. Mohammad Amir Hossain , Yeasmin Selina
Published: April 2023

Objective: The pollution free environment is required for healthy life. The real-time radiation monitoring is very important for radiation hazard detection in the environment. The excess life-time cancer risk (ELCR) on public is to assess based on the real-time radiation monitoring data in the area. Methods: The real-time radiation monitoring was performed using portable digital radiation monitoring device. This real-time digital portable radiation monitoring device meets all European CE standards as well as the American “FCC 15 standard”. The real-time digital portable radiation monitoring device was placed at 1 meter above the ground on tripod and data collection time for each monitoring point (MP) was 1 hour. 27 MPs were chosen for collection of real-time radiation data at various outdoor environment in Motijheel Thana, Dhaka from May-August 2018.Results: The real-time radiation dose rates at Motijheel Thana due to natural radionuclides were ranged from 0.095 ± 0.041 µSv.h-1 to 0.185 ± 0.042 µSv.h-1 with an average of 0.147 ± 0.047 µSv.h-1. The annual effective dose to public from outdoor environmental radiation at Motijheel Thana were found to be 0.166 ± 0.066 mSv to 0.324 ± 0.061 mSv with an average of 0.257 ± 0.039 mSv. Excess Life-time Cancer Risk (ELCR) on public are also estimated based on annual effective dose that is ranged from 0.662 ×10-3 to 1.289 ×10-3 with an average value of 1.025 ×10-3, which is higher than world average value of 0.29×10-3. Conclusion: This type of study is required for detection of the radiation hazard arising from the natu-ral as well as man-made sources and also for generation of the baseline database. From this study, it is observed that there is no pose any radiation hazard in the study area due to man-made sources.