bluuebunny/crossref_metadata_2025 huggingface dataset:
Inexpensive Detection of Substance Abuse Based on Social Media Data using Machine Learning
Authors: Abhinav Potineni
Published: May 2022

Over the past few years, substance abuse has become one of the most severe public health problems in the United States. The annual cost of substance abuse aftereffects in the United States alone is approximately $3.73 Trillion. The societal costs of substance abuse include premature deaths, lost productivity, and increased crime rates. Unfortunately, many victims, especial¬ly in lower-income families, don't have access to early detection and early family intervention tools due to limited access to traditional diagnostic tools and rehab specialists. Currently, there is no complete diagnostic pipeline to inexpensively detect substance abuse and automatically inform family members or trusted contacts. To combat this, the experimenter developed the SOS 280 system, which utilizes machine learning techniques in a smartphone application. SOS 280 works through social media monitorin¬g and automatic notification using SMS and GPS location. The SOS280 algorithm primarily uses social media data, namely publicly available Twitter, and Instagram posts, to identify substance abuse-related activity. The experimenter collected and classified data by applying for the Twitter and Instagram Developer API Platforms, mining tweets and posts with specific drug keywords present. The investigator trained a Natural Language Processing (NLP) text classification model to analyze the sentiments on the tweets, then classifying them as positives (containing substance abuse-related keywords) and negatives. The master model is a Bidirectional Encoder Representations (BERT) derivative that uses a transformer-based architecture to detect emotions in sentences and conversations to classify substance abuse instances. In total, the researchers looked at 55,551 tweets and Instagram posts indicative of potentially alarming substance usage. Finally, the experimenter developed a smartphone application to capture trusted contact information and GPS location, send data to a remote server housing the neural network, output the network's detection, and send automated alerts to trusted contacts via SMS and GPS location. The experimenter further validated the system's effectiveness through a partnership with national nonprofit Faces and Voices of Recovery, which works with 23 million addiction recovery victims. SOS280 is an inexpensive, reliable, easy to use, and timely tool for families of young adults in predicting substance abuse.
An Analysis of Clustering Techniques
Authors: Pradeep Bolleddu
Published: May 2022

I analyzed different clustering methods of credit card customers data by some machine learning clustering algorithm i approached to classify the high dimensional data by using clustering algorithms in the real world ,It is a type of process where we divide the similar objects into one group The Objective of paper is to identify which method gives a general view of clustering techniques like K-means, algometrive and gaussian distribution ,to analyze Segmentation of customers can be used to define marketing strategies. Clustering comes under unsupervised learning, if we take data set it contains some input patterns, entities etc. The main aim of clustering is to make partition & to make clusters having similarity, so that we can get some useful insights and make further analyses .The process of finding closely connected and fair information from high dimensional datasets is complex and difficult to carry .In this paper we tried to analyze our model through different clustering models like K-means, DBSCAN, Agglomerative Hierarchical and gaussian mixture model) and I explained about all algorithm that I used unique approaches for clustering the high dimensional data.
Automated Assessment Generation and Student Performance Prediction In E-Learning System
Authors: Vedashree Lahoti  , Dr. Anjali Raut
Published: May 2022

In this project we proposed an automatic student performance and assessment generation models in e-learning using machine learning algorithms. Our proposed model will find out Students performance by using their behavior. Behavioral data like study material searching, video accessing time, and submission dates, assignment marks, question asking behavior etc. will be tracked into the database. In e-learning assessment generation is a very important and time consuming activity for teachers. Therefore to solve this issue we proposed automatic assessment generation model which will use Formal Concept Analysis algorithm. Formal Concept Analysis algorithm will be used to extract knowledge from the question-answers. A Learning Management System(LMS) is an application software that plays a vital role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses simultaneously.
Information Sharing Management System Based on Blockchain Using Deep Reinforcement Learning
Authors: Ms. Shruti Belsare  , Dr. A. B. Raut
Published: May 2022

In recent years, the food supply system has become increasingly globalized. Traditional traceability systems have issues with centralized administration, opaque information, untrustworthy data, and the ease with which information islands can be created. To address the aforementioned issues, this study proposes a blockchain-based traceability system for storing and querying product information throughout the agricultural supply chain. Most existing systems, on the other hand, are unable to meet the traceability and management requirements of ASCs. To address these concerns, we first develop a blockchain-based ASC architecture for product traceability, which provides decentralised security for agri-food tracing data stored in ASCs. A Deep Reinforcement Learning based Supply Chain Management (DR-SCM) system is then offered to make effective judgments on the production and storage of agri-food commodities for profit optimization. In a variety of ASC scenarios, extensive simulation experiments are conducted out to demonstrate the efficacy of the proposed blockchain-based infrastructure and the DR-SCM strategy.
Social Network and Data Security Issues
Authors: Dr. Magdi Mohammed Mohammed Ahmed Hamoda
Published: May 2022

Computer security is a branch of technology known as Information Security, as it is applied to computers and networks. The objective of computer security involves the protection of information and property from theft and corruption, or natural disasters, while information and property can remain productive and accessible to their intended users. Computer system security terminology means the collective processes and mechanisms through which information, sensitive and valuable services are protected from dissemination, and tampering with or collapsing caused by unauthorized or unreliable activities, and unplanned events, respectively to solve the problems of information security on the social network website. We have to learn about the basic concept and structure of the social network website and its associated social network. The social network of services in the Internet zone usually refers to: social network service, social networking programs and social network website. Clearly, these three things are interlinked and indispensable; they are combined to form a platform for users to communicate information.
Mathematical-Computational Optimization Methods on Primary Molecular Effect Model for Selected High Temperature Superconductors with Electronics Physics Applications
Authors: Francisco Casesnoves
Published: May 2022

In previous series of contributions, Inverse Least Squares (ILS) Numerical Optimization, 3D/4D Interior Optimization, 3D/4D Graphical Optimization software and algorithms-programming were presented. The BCS theory Isotope Effect applications on superconductors TC dual/multiobjective optimization was the main subject. This contribution deals with the Molecular Effect Model for High Temperature Superconductors (HTSCs), in primary hypothesis stages. Selected group of HTSCs is Sn-Sb-Te-Ba-Mn-Cu-O . Namely, classical Inverse Least Squares, 2D Interior Optimization, and 2D Graphical Optimization techniques are applied. Results comprise Tikhonov Regularization algorithms and mathematical methods for this HTSCs group. Mathematical-programming findings to optimize this Type II HTSCs are presented with ILS outcoming data in Matlab and GNU-Octave. Rulings present an hypothesis of new ‘Molecular Effect’ model/algorithm, intended be proven for this HTSCs group. In this Molecular Effect optimization, Inverse Least Squares and Inverse Least Squares polynomial methods are applied with acceptable Numerical and 2D Graphical Optimization solutions. Results show accuracy with low programming residuals that confirm these findings. Solutions comprise two strands, the modelling for Molecular Effect, and the Inverse Least Squares improved programming methods with confidence intervals and statistical images. Hypothesis in Electronics Physics applications for Superconductors and High Temperature Superconductors emerge from findings. Modelling-hypothesis applications in Superconductor Theory turn up from the numerical and imaging data obtained.
Crop Prediction and Efficient use of Fertilizers using Machine Learning
Authors: Rohit J Kashyap  , Dr. Sivakumar V  , Rachel Rose Oommen  , Darshan A  , Bhoomika R
Published: May 2022

India being an agricultural country, its economy predominantly depends on agriculture yield growth and allied agro industry products. In India, agriculture is largely influenced by rainwater which is highly unpredictable. Agriculture growth also depends on diverse soil parameters, namely Nitrogen, Phosphorus, Potassium, Crop rotation, Soil moisture, and Surface temperature and also on weather aspects which include temperature, rainfall, etc. India now is rapidly progressing towards technical development. Thus, technology will prove to be beneficial to agriculture which will increase crop productivity resulting in better yields to the farmer. The proposed project provides a solution for Smart Agriculture by monitoring the agricultural field which can assist the farmers in increasing productivity to a great extent. Weather forecast data obtained from IMD (Indian Metrological Department) such as temperature and rainfall and soil parameters repository gives insight into which crops are suitable to be cultivated in a particular area.
Soil Moisture Counter in Smart Farming
Authors: Aarush Narendra Gedam
Published: May 2022

Agriculture has been the primary occupation in our country for ages. But now due to migration of people from rural to urban there is hindrance in agriculture. To overcome this problem, it will be better to go for smart agriculture techniques using IoT. To bring this concept into reality, various sensors are used and deployed at different locations in the farm. IoT gives a platform to researchers to maintain real time data. Soil moisture monitoring is an area of growing study for dryland farming and irrigators alike. Accurate monitoring of soil moisture penetration into the soil depth profile is key to efficient and effective irrigation techniques by access to information that comes from sensors. In this paper we are showing practical demonstration of Soil Moisture Counter in Smart Farming so that effective irrigation can lead to increased yields by preventing plant stress days and keeping nutrients in the fibrous root zone.
Comprehensive Study On EDGE-Cloud Collaborative Computing for Optimal Task Scheduling
Authors: K. Vinothkumar  , Dr. D. Maruthanayagam
Published: May 2022

In recent years, Cloud and edge computing have got much attention because of the ever-increasing demands. There are many future technologies and advantages for systems to move towards clouds based on information keep methods. This includes a simple IT substructure and administration, and an effective distant approach from any place in the global with the steady computer network connections and efficient cost that cloud engineering can give. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud edge based environment is resource management, which typically revolves around resource allocation, resource provisioning, task scheduling and improve performance. Aiming at the future problem of simulating service requests and optimal task scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature (quality) mining analysis, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support. The main goal of this paper is to provide the better and deeper understanding regarding the scheduling approaches in the Edge-Cloud environment that covers the way in the scheduling approaches.
An IoT System Prototype for Implementation of Secure Smart Agriculture
Authors: Mr. Siddhunath Jalpur  , Mrs. Madhuri Gedam  , Dr. Roopali. Lolage  , Dr. Nazneen. Ansari
Published: May 2022

The Internet of Things (IoT) has the power to change our world. Agriculture is the backbone of the India. Development of IoT application is a must, for solving problems that are faced by farmers in the field of agriculture. Lack of proper information and communication leads to the loss in production. In the proposed system, we have automated the drip irrigation technology with the help of IoT and optimizing the usage of water level. In this paper, we are providing most secured MQTT protocol at the database level (Mongodb) and application level with the encryption technic for data security.
A Survey on Anti-Spoofing Methods for Facial Recognition
Authors: Manoj G  , Yashas D. S  , Jeevan K. P  , Likith M  , Dr. Raghavendra R. J
Published: May 2022

Despite significant development in facial recognition (FR), current FR systems are exposed to spoofing attacks like printed photo attacks, 3D mask attacks, video replay attacks, and many more. Several anti-spoofing approaches have been proposed to assess whether the person in front of the camera is real or fake. Developing effective protection mechanisms against these threats is a challenging task. This paper gives a brief overview of various presentation attack detection (PAD) techniques, which are categorized into intrusive and non-intrusive approaches. Each technique is examined in terms of its execution, benefits, and drawbacks and also provides information on modern anti-spoofing techniques.
Visualization of the Virtual Tour by Stitching 360 Panoramic Images
Authors: Prof. Priyanka B. Dongre  , Saurabh Agade  , Abhishree Gonnade  , Khushada Patil  , Shruti Selokar
Published: May 2022

During the COVID pandemic and the series of closures it has introduced, 360 Images virtual travel allows individuals to remotely experience cultural places and artifacts from the safety of their homes. A popular form of virtual tourism is 360-Images virtual tours: digital experiences that render a 360-Images visual representation of the real-world environment on the users’ personal device. Despite the increasing popularity of 360 virtual tours, there is no common conceptual framework for creating them. In our work, we analyzed multiple existing virtual tours and constructed a 360 Images framework for virtual tour design. We give you a platform to attach your 360 Images in a Virtual tour manner and add detail about it by using our 360 images virtual tool, then you are ready to use a Virtual tour by adding links on social media or websites.
Reconfigures Protected Protocols for Advanced Cloud Computing
Authors: Mohammed Inamur Rahman  , Shamimul Qamar  , Abdulilah G.F Saif  , Abdul Azeem
Published: May 2022

Cloud computing is a term which used to depict both a stage and kind of utilization. Cloud computing varies from customary registering ideal models as it is adaptable, can be embodied as a conceptual these elements which gives us diverse level of administrations to the customers, driven by economies of scale and the administrations are progressively configurable. Information put away in outsider stockpiling frameworks like the cloud probably won't be secure since classification and honesty of information are not ensured. In spite of the fact that distributed computing gives savvy stockpiling administrations. Subsequently, numerous associations and clients may not utilize the cloud administrations to store their information in the cloud until the point that specific security ensures are made. In this paper, an answer for the issue of safely putting away the customer's information by keeping up the privacy and trustworthiness of the information inside the cloud is produced. The proposed conventions are created which guarantee that the customer's information is put away just on confided away servers, reproduced just on confided away servers, and certification that the information proprietors and other favored clients of that information get to the information safely.
Optimization in Security of Digital Evidences by Integration of Evidence Integrity Assurance Mechanism (EIAM) Plug-in into the Software Framework of Mobile Forensic Tools with Balanced Use of Antivirus Softwares
Authors: Manjre Bhushan M. , Kumar Goyal Dr. Krishan , Shivani Dr.
Published: May 2022

In today's era, there exists variety of mobile forensic tools both proprietary as well as open source tools and this generation of mobile forensic tools is evolving at a faster pace with new features which mainly focuses on the deep penetration into the mobile handheld to optimize the mobile forensic process and mainly extraction and decoding of mobile artifacts. But a bit less attention is provided towards the integrity of the digital evidence obtained. There are many factors that can alter the data and the use of antivirus software is one of the prominent factors among them. The importance of antivirus software in the machine could not be denied and hence its systematic use if done, will not only save the digital evidence from the malwares but also saves it from the antivirus software itself. The proposed work describes how the EIAM (Evidence Integrity Assurance Mechanism) plug-in handles the antivirus software in a smart optimized way.
Image Pre-processing techniques comparison : COVID-19 detection through Chest X-Rays via Deep Learning
Authors: Rajvardhan Shendge  , Tejashree Shengde
Published: May 2022

The COVID-19 pandemic had a particularly devastating effect, spreading rapidly over the world and infecting about 36 million individuals. Chest radiography is a critical component that aids in the early detection of a variety of diseases. With the spread of the pandemic, training Convolutional Neural Networks (CNN) to detect and identify COVID-19 from chest X-rays is becoming more popular. However, there are few publicly available and medically validated datasets for COVID-19 infected chest X-Rays, resulting in the model failing to generalize successfully. It is critical to pre-process and enrich the data used to train the model in order to achieve this aim. Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Top Bottom Hat Transform are some of the pre-processing techniques available. In this study, we examine and compare all of these pre-processing methods in order to determine which is best for building a CNN model that can accurately classify an image as infected with COVID-19 or Viral Pneumonia.
Impact of Computer Aid Instructions on Locus of Control And Students’ Performance In Mensuration Of SSS One Students In Kagarko Local Government Area of Kaduna State, Nigeria
Authors: Aliyu Alhaji ZAKARIYYA
Published: May 2022

The study investigated the affects of Computer Aided Instruction (CAI) on students’ performance and locus of control in learning geometry of SSS one students. The objectives of the study were: To investigate the effect of CAI on students’ performance in geometry when compared with conventional method (CM), to establish gender difference in performance using CAI, to assess students’ locus of control in the learning when taught using CAI and CM. The study adopted quasi experimental design. The study has population of 4982 students, consisting of 2678 males and 2304 females. The sample of the study consisted of 67 male and 62 female. There are 65 students in the experimental and 64 students in the control groups respectively. Four instruments were used for data collection; Student’s Locus of Control, pre-test, post-test, lesson plan based on computer aid instruction and lesson plan based on conventional method. Student’s Locus of Control questionnaire. The participants were taught volumes of cylinder and cone for six weeks. The Cronbach’s Alpha of the tests was estimated to be 0.720 and 0.801 respectively. The differences between the groups means was analyzed using t-test and Mann-Whitney U-test, at ? = 0.05. The study revealed that, the students taught geometry with CAI obtained higher geometry scores and higher locus of control scores than the students taught with CM. The study further revealed that there were no gender differences in CAI group. It was recommended that mathematics teachers should use CAI and train students to develop internal locus of control.
A Review on MY-SR Next Generation Automated Gas Safety System Using IoT
Authors: Dr. Komala C R  , Yashashwini S  , Rashmi Nayak  , Sharanya S  , Megha P  , Prof. Priya J
Published: May 2022

Smart embedded systems have become a core component in the latest technologies, and IoT based smart embedded system is the trendiest field in the research area. In our research, we are proposing an IoT based smart stove. Any accident might occur at any time from a stove. So, we are designing a two-way safety enabled stove with a child lock system and gas leakage detection feature open the door or window. The intelligent stove will try to ensure safety and will detect age from real-time video streaming. Our main focus is a child would not be able to turn the stove on. As well as, the stove can entitle safety via gas detection alarm. Automatic gas booking system once read the load cell has less value. We are using a Arduino Uno and Gas Detection Module with a buzzer for the hardware implementation. Also, we are applying a Machine Learning object detection algorithm (Haar Cascade) and a deep learning architecture (CNN) for the system execution. Since our stove is IoT-based, the stove is ensuring safety remotely as well as manually which will try to prevent accidental occurrences.
Intelligent Quality Monitoring System for Food Grain Storage Using IoT
Authors: Prof Khallikkunaisa  , Ayesha Nabeela  , Asra Firdows  , Harshitha M  , Shwetha Ramadas
Published: May 2022

A Developing country like India which is among the most important agricultural manufacturer within the globe has about 179.9 million hectares of its land under cultivation. About 70% of the rural population relies on cultivation for their livelihoods, since it is their sole source of income. The total food grain production in India is projected to increase 2% by the year2023 with about 303 million tons being produced each year which is a lot compared to other counties. A major problem stems from the fact that most of these food grains are mass-produced, which causes them to be wasted if they have been damaged by rains, floods, variations of temperature and humidity, attacks of rodents, insects. It is imperative to reduce the number of food grains being wasted, which can be achieved through the application of proper storage techniques. This is accomplished with the aid of automation, such as the Internet of Things (IoT), which enables continuous monitoring and tracking of food grains at storage warehouses. Hence, the proposed method utilizes an IoT System with sensors that collect data such as humidity, temperature, and C02 percentage. The data is notified to the user when parameters change and control measures are taken to ensure that the food grains remain in optimum conditions for the best quality.
Development of Applicability of Distribution Factor for Single Cell Box Girder Bridges by Modifying Bakht and Mufti Method
Authors: Sreevar Ramesh  , R K Ingle
Published: May 2022

Live load distribution factors for simply supported single cell box girder by manual method has been studied. The distribution factor allows the designer to consider the transverse effect of wheel loads in determining the shear and moments of each beam for live load. The increased need of transporting very heavy loads by road haulage has given rise to the need for an easily applied and accurate method of calculating the division of loads between the main girders of highway bridge decks. The bridge is loaded with IRC 70 R and Class A loading so as to obtain maximum bending moment. Bakht and Mufti is close representation of semi-continuum idealization to obtain distribution factors for slab on girder bridges where shear deformation or more correctly cell distortion is neglected. An attempt is made to obtain distribution factor for single cell box girders by modifying the formula of Bakht and Mufti method with Cusens and Pama formula to consider distortional effects. Results are compared with spine beam method using Midas Civil Software which shows good agreement between both the methods.
Smart Health Consulting System Using Machine Learning
Authors: Rezni S  , Syed Umar  , Umme Hajira  , Umair Amin  , Syed Nabeel Sultan
Published: May 2022

For the treatment and prevention of sickness, accurate and timely investigation of any health-related problem is critical. In the case of a critical illness, the standard method of diagnosing may not be sufficient. People nowadays suffer from a variety of ailments as a result of the environment and their lifestyle choices. As a result, predicting sickness at an early stage becomes a critical responsibility. However, doctors find it challenging to make precise predictions based on symptoms. The most difficult challenge is correctly predicting sickness. The development of a diagnosable disorder method machine learning - based (ML) algorithms for illness prediction can aid in a much more official diagnosis than the current technique. Using numerous machine learning techniques, we created a disease prediction system.
A Survey on Sentiment Analysis and Topic Modeling
Authors: Liyansi Patel  , Vimal Rathod
Published: May 2022

Sentiment Reason Mining is an emerging research area in this era of social media. Sentiment Reason Mining aims to resolve two problems: first is finding the reason of a sentiment, and second is interpreting sentiment variations. Time and Event where sentiment is being changed is also an important factor. Aspect-Based methods, Supervised Learning, Topic Modeling, and Data Visualization etc. can be used for finding the reason of a sentiment. VADER Sentiment Classifier can be used for sentiment of tweets. LDA is topic Modeling algorithm. In this research paper we have reviewed some the research work performed for this purpose. We have reviewed various research work which have used social media content as dataset. TF/IDF feature extraction is used in most of the work. Sentiment Detection tools VADER and Text Blob are also discussed in our work.
Voltage Recovery from Pesticides Doped Tomatoes, Cabbages and Loam Soil Inoculated with Rumen Waste: Microbial Fuel Cells
Authors: Kinyua A.  , Mbugua J. K  , Mbui D.N  , Kithure J.  , Michira I  , Wandiga S.O
Published: May 2022

In the current study, voltage generation from loam soil, cabbages and tomatoes for a retention time of thirty days using double chamber microbial fuel cell is investigated. The anodic with 1.5 liters’ substrate inoculated with rumen fluid from slaughter house and cathodic loaded with distilled water compartments were connected via a salt bridge made using 3% agarose in sodium chloride. The performance of microbial fuel cells was evaluated by measuring daily voltage using a multimeter. The observed results showed that the current produced increased for some time and levelled off on the tenth day. Current was highest in rumen fluid set up at 0.111±0.003 mA followed by tomato fruits, loam soil and least in cabbage at 0.101±0.008, 0.095±0.001 and 0.094±0.007 mA, respectively.
A Survey on Fake News Detection using Support Vector Model
Authors: Himani Kanojia  , Prof. Rahul Vaza
Published: May 2022

In the boosting period of Social Media availability and easy availability of Internet to end users in various regions, many challenges are also occurred with usage of this technology. Fake news spreading in various field is also a major challenge in recent time. Fake news has been spreading into vast in significant numbers for various business reasons and also for political reasons. Problem of fake news has become frequent in the online world. People can get affected and their view are also affected easily by these type of fake news for its fabricated words. This type of news has enormous effects on the offline community in various sectors. In this way it is an interesting topic for research. Significant research has been conducted on the detection of fake news from English texts and other languages but there is chancel to improve the work with other languages as well as from multiple sources. Various algorithms like SVM and other supervised algorithm can be helpful to classify fake news. As Sentiment Score is an also a major point for detection of Fake news; in our work we are applying SVM algorithm with TF/IDF, multiple Languages (Language Conversation) etc.”
Hemoglobin Level and Total Differential Blood Leucocytes of Broiler Chicken Given Different Probiotics
Authors: Rusli Badaruddin  , Harapin Hafid  , La Ode Nafiu  , Syamsuddin  , Amiluddin Indi  , Fuji Astuti Auza
Published: May 2022

The use of adding probiotics to drinking water is an alternative to reduce the accumulation of additional chemical feed residues in meat, which functions to maintain the balance of the microflora ecosystem in the digestive tract and provide enzymes that can digest crude fiber, protein, fat and detoxify toxic substances or metabolites. This study aimed to determine the hemoglobin level and total differential leukocyte blood of broiler chickens given different probiotics using 100 broiler strains of MB 202 platinum with 20 cages with a size of 80X80 cm. The research design used a completely randomized design (CRD) with 4 treatments and 5 replications consisting of 5 chickens for each replication. The results showed that blood hemoglobin levels had a significant effect (P>0.05) and the administration of different probiotics into drinking water had no significant effect (P>0.05) on the differential value of leukocytes (monocytes, eosinophils, heterophils, and basophils) in broiler blood, but giving different probiotics had a significant effect (P<0.05) on blood lymphocyte levels in broiler chickens.
Addition of Aspergillus Niger Palm Waste (Animal Feed Alternative)
Authors: Syamsuddin  , Harapin Hafid  , Rusli Badaruddin  , Amiluddin Indi  , Natsir Sandiah
Published: May 2022

The study aimed to determine the effect of adding the fungus Aspergillus niger to the chemical quality of fermented palm oil leaf and midrib waste as an alternative feed ingredient. The research design used a Completely Randomized Design (CRD) in one direction with four treatments and four replications. The treatment given to oil palm leaves and midribs was P0 = oil palm leaves and midribs + 20% EM4, P1 = oil palm leaves and midribs + 20% EM4 + 1% Aspergillus niger, P2 = oil palm leaves and midribs + 20% EM4 + 2% Aspergillus niger, P3 = oil palm leaf and midrib + 20% EM4 + 3% Aspergillus niger. This fermentation was carried out using the Least Significant Difference Test (SDT) method. The results showed that the fermentation of oil palm leaves and midribs could significantly increase the crude protein content in the P2 treatment (10.32%) and organic matter in the P1 treatment (29.09%), but had no significant effect on dry matter and crude fiber.
Analysis of Family Working Time Integration of Bali Cattle and Rice Fields in The District South Konawe
Authors: Musram Abadi  , La Ode Arsad Sani  , Lukman Yunus  , Saediman
Published: May 2022

The research aims to find out the allocation and outpouring of family working time in integrated Bali cattle business rice paddy in South Konawe Regency collected from bali cattle farming households as many as 30 respondents each sub-district of 25 subdistricts in South Konawe Regency. The outpouring of working hours involved in the handling of Bali cattle and rice paddy farming is calculated using the unit of working hours equivalent to adult men (JKSP) with the unit of working people's days (HOK). The results of the analysis were dominated by a semi-intensive maintenance system with a breeding pattern with an average outpouring of family working time in Bali cattle businesses of 0.74 HOK, while the average outpouring of family working time on rice paddy farming was 10.34 HOK. Family labor in the central area of Bali cattle seedlings in South Konawe Regency devotes more time to rice paddy farming (93.30%) than Bali cattle business (6.70%).
A Framework for Predicting and Analyzing Fake News Using Machine Learning
Authors: Vaishali Singh
Published: May 2022

In today's world, social media is the most effective way to express him. And this is the finest area to provide information about yourself, your society, your faith, and your customs. It is involved in the rapid exchange of information, in which news from all fields is available. Social media has a major impact on our lives and society nowadays. And, in today's world, social media is the most effective way to express him. Furthermore, social media has evolved into a platform for sharing current events. People in the other location are informed about what is going on in the other location. People also learn about the culture of other places as a result of this. However, some nefarious elements utilize social media to promote false information, which has an impact on both our lives and society. And if Fake News isn't dealt with quickly enough, it spreads like a forest fire. And this fake news hurts some people's sentiments, and it has also been known to trigger riots in society. It is vital in today's world to have some instruments that can verify any news, whether it is factual or not. And I'd like to accomplish the same thing with this algorithm.
Prediction of Chronic Kidney Disease-A Machine Learning Perspective
Authors: Kantharaju. V  , R. Pavithra  , Nisarga H  , Karishma S
Published: May 2022

Chronic Kidney Disease is one of the most critical illnesses nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on the following factors given below: (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv)Least absolute shrinkage and selection operator regression, (v) synthetic minority over- sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority oversampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F- measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.
Machine Learning based data analytics for IoT enabled Industry Automation
Authors: B. Manikandan  , D. Divya Dharshini  , V. Ramya  , S. Jayalakshmi  , M. Srimathi
Published: May 2022

The main aims of this projects to the replacement of old communication that uses wired links with new communication that is wireless communication.The main reason to move to wireless communication is to improve the mobility, reduce the deployment cost, reduce cable damage and to improve the scalability.The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence. The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence.The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence.The second aim of this project is to connect devices to IOT so as to improve theaccessibility of the industry from anywhere in the world. These services are known as Best Effort services.
Phytochemical Screening and In-Vitro Antioxidant Activity of An Herbal Drug ‘Ashwagandharishta’, Estimation of Total Phenolic and Flavonoid Contents
Authors: R. K. Pawar  , Dr. G. R. Bhagure
Published: May 2022

Because of their ability to neutralise free radicals, phenolic compounds have a variety of biological effects, including anti-inflammatory, antibacterial, and antioxidant characteristics. Antioxidants are essential nutrients that protect the body from the harmful effects of oxidative stress caused by free radicals. A wide range of free radical scavenging antioxidants can be found in a variety of foods. The study's goal was to find out how much total phenolic content, total flavonoids content, antioxidant activity, and phytochemical screening of herbal drugs. Flavonoids are a type of natural polyphenolic chemicals that humans are unable to produce. These compounds have a variety of biological characteristics and act as antioxidants in biological systems. The Folin-Ciocalteau method was used to determine the total phenolic content. Antioxidant properties were determined using 1,1-diphenyl-2- picrylhydrazyl (DPPH). The total phenolic and flavonoid content was determined spectrophotometrically using gallic acid and quercitin as standards. The Herbal drug for the study is purchased from the local markets of Nashik (Maharashtra). 5 percent, 2.5 percent, 1.25 percent, 0.625 percent, 0.3125 percent, and 0.15625 percent concentration samples in water of the herbal medication were prepared for the assessment of antioxidant potential by DPPH assay. As the concentration % rises, so does the antioxidant activity, as well as the overall phenolic and falvonoid content increases. The antioxidant activity is due to the existence of total phenolic and flavonoid content. Conventional methods were used to determine the presence of specific phytochemicals.
Transfer Learning-Based Approach for Early Detection of Alzheimer's Disease
Authors: G Nagarjuna Reddy  , M Subhash Chandra Bose  , K Vamsi Krishna  , M Rakesh Reddy  , K Ajay
Published: May 2022

Alzheimer's disease is one of the world's main health concerns today. People with Alzheimer's disease who are diagnosed early have the best chance of receiving effective therapy. It's critical to catch the sickness as early as possible. Magnetic resonance imaging is one way to define Alzheimer's disease by finding structural abnormalities in the brain (MRI). We propose that machine learning, specifically trained convolutional neural networks (CNNs) with transfer learning capable of making predictions about similar brain imagery, can aid in early detection. CNN enables the extraction of MRI properties and classification as Alzheimer's disease or normal brain. We used the VGG19 architecture to categorize patients as having no signs of Alzheimer's disease or having signs of very mild, mild, or moderate Alzheimer's disease. Based on a transfer learning methodology, this method correctly classifies MRI images into four phases of Alzheimer's disease with an accuracy of 85 percent.
An Intelligent Motion Detection Using OpenCV
Authors: Shubham Mishra  , Mrs. Versha Verma  , Dr. Nikhat Akhtar  , Shivam Chaturvedi  , Dr. Yusuf Perwej
Published: May 2022

A computer vision system's basic goal is to detect moving things. For many applications, the performance of these systems is insufficient. One of the key reasons is that dealing with numerous restrictions such as environmental fluctuations makes the moving object detection process harder. Motion detection is a well-known computer technology associated with computer vision and image processing that focuses on detecting objects or instances of a specific class in digital photos and videos (for example, humans, flowers, and animals). Face detection, character recognition, and vehicle calculation are just a few of the well-studied applications of object motion detection. Object detection has a wide range of applications, including retrieval and surveillance. Object counting is a step after object detection that gets more exact and robust with the help of OpenCV. For object detection and counting, OpenCV includes a number of useful techniques. Object counting has a variety of applications in the fields of transportation, medicine, and environmental science, among others. Computer vision and image processing research is progressing rapidly and is being used to improve human lives. To avoid the drawbacks of current and newly established techniques, the suggested algorithm was tested on many open source images by imposing a single set of variables. The motion detection software system proposed in this paper allows us to see movement around an item or a visual area.
Computational Modelling of Musculoskeleton to Predict Human Response with Upper Arm Exoskeleton
Authors: Dr. Balaji B  , Dr. Abhishek M R
Published: May 2022

There are many situations where elderly people find it difficult to do their daily work, stroke affected people face difficulty in doing their daily routines independently, soldiers of the country also face problems in carrying heavy loads in rough terrains for long time. An external wearable outfit which increases the capabilities of a normal human being is called the exoskeleton. To design and manufacture an upper arm exoskeleton for upper extremity of human body, computational studies and analysis is to be made as the working model is a wearable device, it should not harm the user in any condition. To analyze the effect of wearing an upper arm exoskeleton, an inertial measurement unit is placed on a subjects forearm at the center of mass point. The specified arm movement considered in sagittal plane is performed wearing the IMU so that the angular displacement readings are extracted. The extracted data is fed to a human body simulation software lifemod where the human model does the same arm movement as the subject. Kinematic parameters are plotted from the software, to check whether the software’s reading is appropriate a mathematical model is developed using lagrangian equation for the same arm movement in sagittal plane and the torque equations obtained are solved using matlab software. Both the plots of kinematic parameters are compared. The plots from both software’s match perfectly proving that the simulation software readings are near to reality. Muscle activation plots without exoskeleton condition are plotted for different weights and for different wrist positions. An upper arm exoskeleton is designed in ADAMS software and is imported to the model and analysis is made while lifting different weights and for different wrist positions. Muscle activation plots are obtained for all the cases with exoskeleton condition. Muscle activation values from with and without exoskeleton condition proves the usefulness of using an exoskeleton. The muscle activation is very low in case while using an exoskeleton which shows that with minimal human effort more work can be done.
Implementation of Sentimental Analysis Using Twitter Data
Authors: Prof. Pradeep N. Fale  , Uddesh shelkhi  , Vaibhav Kosare  , Saurav Chaudhari  , Vinay Sharma
Published: May 2022

Online media are becoming extra attention those days. Public and personal evaluation on a extensive collection of topics are communicated and unfold always by diverse on line media. Twitter is one of the on line media this is obtaining prominence. Twitter gives association’s short and compelling method to research clients' viewpoints closer to the primary to accomplishment within side the industrial Centre. Building up a software for belief research is a manner to cope with be applied to computationally degree clients' insights. This paper investigates the plan of a end examination, setting apart a massive degree of tweets. Prototyping is applied on this flip of events. Results set up clients' factor of view thru tweets into fantastic and negative, that is addressed in a pie define and html page. Nonetheless, this system has desired to create on an internet utility framework, but due to restrict of Django which may be bartered with a Linux employee or LAMP, for added this system ought to be finished.
Essay Scoring Model Based on Gated Recurrent Unit Technique
Authors: Eluwa J.  , Kuyoro S.  , Awodele O.  , Ajayi A.
Published: May 2022

Educational evaluation is a major factor in determining students’ learning aptitude and academic performance. The scoring technique that relies solely on human labour is time consuming, costly, and logistically challenging as this rating is usually based on the opinion of “biased” human. Several studies have considered using machine learning techniques with feature extraction based on Term Frequency (TF) - Part of Speech (POS) Tagging without consideration to global vectorization (GloVe). These solutions require the process of selecting deterministic features that are directly related to essay quality which is time-consuming and needs a great deal of linguistic knowledge. Gated Recurrent Unit (a variation of Recurrent Neural Network) deep learning technique with focus on morphological analysis of essays for content-based assessment has therefore shown the capability of addressing the challenges posed by other AES techniques by building more abstract and complete linkages among features.

Deep learning algorithms such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were used to learn the model with performance evaluation on metrics such as validation accuracy, training time, loss function, and Quadratic Weighted Kappa. The performance results showed that MLP, LSTM and GRU had average Quadratic Weighted Kappa (QWK) values of 0.65, 0.86 and 0.88 respectively with each algorithm having an average training time of 61.4, 62.68 and 67.86 seconds respectively. The loss functions for MLP, LSTM and GRU were 0.296, 0.24 and 0.126. This meant that GRU had the best estimate of the difference between the actual and forecasted scores. MLP, LSTM, and GRU had average validation accuracy of 0.48, 0.537, and 0.511 respectively. GRU was shown to be the optimal classifier and was used in the development of the essay scoring model.