59 skills found · Page 1 of 2
NAalytics / Assemblies Of Putative SARS CoV2 Spike Encoding MRNA Sequences For Vaccines BNT 162b2 And MRNA 1273RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
JGAguado / Smart PowermeterThe Smart Powermeter is powered by a ESP32-S2, allowing the measurement up to 6 CT clamps for reading electric current and real-time display on an 2.9" e-paper display.
Ali-Alhamaly / Turbofan Usefull Life Predictiongiven run to failure measurements of various sensors on a sample of similar jet engines, estimate the remaining useful life (RUL) of a new jet engine that has measurements of the same sensor for a period of time equal to its current operational time.
sophierahn / LiB SOH PredictionA prediction model to estimate the state of health (SOH) of a lithium-ion battery (LiB) in real-time based on temperature, voltage, and current measurements. Based on the 2007 NASA Ames research center lithium-ion battery data set
mehmetkacmaz / Diameter Measurement Of Circular Geometry PartsThe aim of this program is to make diameter measurements of parts with circular geometry using 5 different methods currently available in the literature. These methods are Pixel Counting, Circle Fitting, Canny/Devernay Subpixel Edge Detection Method, Accurate Subpixel Edge Location Method and Zernike-Moments Based Edge Detection With Sub-Pixel Accuracy.
limingado / NSCThe code is an implementation of the Nystrӧm-based spectral clustering with the K-nearest neighbour-based sampling (KNNS) method (Pang et al. 2021). It is aimed for individual tree segmentation using airborne LiDAR point cloud data. When using the code, please cite as: Yong Pang, Weiwei Wang, Liming Du, Zhongjun Zhang, Xiaojun Liang, Yongning Li, Zuyuan Wang (2021) Nystrӧm-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation, International Journal of Digital Earth Code files: ‘segmentation.py’: the main function, including deriving local maximum from Canopy Height Model (CHM); ‘VNSC.py’: other functions for the algorithm, including mean-shift voxelization, similarity graph construction, KNNS sampling, eigendecomposition, k-means clustering, as well as the computation and writing of individual tree parameters. Key parameters: When using the code, users can adjust the values of local maximum window, gap (the upper limit of the number of final clusters), knn (the number of k-nearest neighbours in the similarity graph) and quantile in meanshift method based specific data characteristics. Currently, the value of local maximum window is 3m ×3m, the value of gap is defined as the 1.5 times of the local maximum detected from CHM. Parameter knn can be defined as a constant value (40 in the code) based on the data characteristics, or be determined through the relationship between it and the number of voxels. The default setting of quantile in meanshift method is the average density of point clouds. More details can be found in Pang et al. (2021). Test data: ‘ALS_pointclouds.txt’: point cloud data; ‘ALS_CHM.tif’: CHM of the point cloud data; ‘Reference_tree.csv’: field measurements for algorithm validation. The position was measured using differential GNSS. The tree height of each tree in this file is obtained by regression estimation. Outputs: ‘Data_seg.csv’: coordinate of each point (x, y, z) as well as its cluster label after segmentation; ‘Parameter.csv’: individual tree parameters (TreeID, Position_X, Position_Y, Crown, Height) based on the calculation described in Pang et al. (2021).
Autofoxsys / AutoFox INA226C and C++ libraries for using the INA226 voltage, current and power measurement chip from Texas Instruments. Built and tested on Arduino and STM32 (using CubeMX, HAL & TrueStudio)
magicgoose / Simple Dr MeterAn (optimized) implementation of the music DR measurement (compliant with http://dr.loudness-war.info/), it supports CUE sheets and is faster than all currently available alternatives (at the time of writing, not sure about now)
AKAGIwyf / UAV TrackingIn recent years, UAV began to appear in all aspects of production and life of human society, and has been widely used in aerial photography, monitoring, security, disaster relief and other fields. For example, UAV tracking can be used for urban security, automatic cruise to find suspects and assist in intelligent urban security management.However, the practical application of UAV in various early scenes was mostly based on human remote control or intervention, and the degree of automation was not high. The degree to which UAVs can be automated is one of the decisive factors in whether they can play a bigger role in the future. With the increasing demand of UAV automation, target tracking based on computer vision has become one of the current research hotspots. Some companies in China and abroad, such as DJI, have successfully equipped target tracking on UAVs, but these technologies only exist in papers and descriptions, and the specific implementation has not been sorted out and opened source. Therefore, we plan to try to complete this project by ourselves and open source it on Github. Traditional visual tracking has many advantages, such as strong autonomy, wide measurement range and access to a large amount of environmental information, it also has many disadvantages.It requires a powerful hardware system. In order to obtain accurate navigation information, it needs to be equipped with a high-resolution camera and a powerful processor. From image data acquisition to processing, huge data operations are involved, which undoubtedly increases the cost of UAV tracking. Moreover, the reliability of traditional visual navigation and tracking is poor, and it is difficult for UAV to work in complex lighting and obstacle scenes. Therefore, we plan to use deep learning for target tracking in this project. We can train our own model through deep learning algorithm (we have not decided what network structure to use), then move the trained model to the embedded development board for operation, fix it on the UAV, read the image through the camera and process the data, so that it can recognize the objects to be recognized and tracked. In this project, we will use NVIDIA Jetson TX2 development board, install ROS in Linux system, establish communication with pixhawk, and conduct UAV flight control through PID algorithm.
NestieGuilas / Marketing Platform Marketing Platform Google Analytics Terms of Service These Google Analytics Terms of Service (this "Agreement") are entered into by Google LLC ("Google") and the entity executing this Agreement ("You"). This Agreement governs Your use of the standard Google Analytics (the "Service"). BY CLICKING THE "I ACCEPT" BUTTON, COMPLETING THE REGISTRATION PROCESS, OR USING THE SERVICE, YOU ACKNOWLEDGE THAT YOU HAVE REVIEWED AND ACCEPT THIS AGREEMENT AND ARE AUTHORIZED TO ACT ON BEHALF OF, AND BIND TO THIS AGREEMENT, THE OWNER OF THIS ACCOUNT. In consideration of the foregoing, the parties agree as follows: 1. Definitions. "Account" refers to the account for the Service. All Profiles (as applicable) linked to a single Property will have their Hits aggregated before determining the charge for the Service for that Property. 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JNDreviews / Est Smartphones Under Rs.15000 WHICH ARE THE BEST SMARTPHONES UNDER 15000 . Best Smartphones under Rs.15000 models 2021 Step by step instructions to track down the best cell phones under Rs.15,000?Take a look Cell phones have turned into a central piece of our life. We can't ponder our existence without cell phones. Assuming you are hoping to purchase a Smartphones under ₹15,000, look at our rundown. There are various cell phones accessible in the various sections yet Smartphones under Rs.15,000 are the most jammed cell phone fragment in the Indian market. We get cell phones that offer fantastic worth and progressed components and execution. The accompanying elements that ought to be thought of while purchasing a Smartphone under Rs.15,000 are battery execution, quick charging, great showcase, nice execution and gaming experience, RAM, Processor, camera, working framework, and all that things are remembered for the underneath cell phones list. Cell phones makers center around making quality innovation that is available for everybody. On the off chance that you are searching for a cell phone in your spending plan, look at the beneath rundown of Best Smartphones under Rs.15,000. Here is the current rundown of Best Smartphones under Rs 15,000: Redmi Note 10 Realme 8 Realme Narzo 30 Samsung Galaxy M32 Motorola Moto G30 Redmi Note 10: WHICH ARE THE BEST SMARTPHONES UNDER 15000 Best Smartphones under Rs.15000 models 2021 Redmi Note 10 is one of the Most outstanding Smartphone under Rs.15000.Redmi has as of late refreshed its Note Series. This gadget accompanies a splendid 6.43 inch full HD show and offers great execution. As far as battery life, this cell phone is the best 5,000mAh battery which can undoubtedly most recent daily, charges from 0 to half inside 30 minutes. It has a super AMOLED show that permits you to encounter a smooth and vivid survey insight. Redmi Note 10 controlled by the Qualcomm Snapdragon 678 SoC processor that is amazing enough for relaxed gaming just as ordinary undertakings. Photography is streamlined with a 48 MP Quad Rear camera with a 8MP Ultra-wide focal point, 2MP Macro, and Portrait focal point on the front 13 MP selfie camera. It can record 4K@30fps, support magnificence mode, slow movement, and different elements. Redmi Note 10 has double sound system speakers with Hi-Res ensured sound for a vivid sound encounter. The side-mounted unique finger impression sensor accompanies a flush plan to give you an exceptional vibe. Presently you can open your gadget effectively with a smidgen. Shields your gadget from unforeseen falls and undesirable scratches with Corning Gorilla glasses. Redmi Note 10 comes in 3 distinctive slick shadings Aqua Green, Shadow Black, Frost white.3.5mm sound jack, simply attachment and play for constant amusement. Specialized Specification: Measurements (mm):160.46 x 74.50 x 8.30 Weight (g):178.80 Battery limit (mAh):5000 Quick charging: Proprietary Tones: Aqua Green, Frost White, Shadow Black Show: Screen size (inches):6.43 Touchscreen:Yes Resolution:1080×2400 pixels Assurance type:Gorilla Glass Processor octa-center Processor make Qualcomm Snapdragon 678 RAM:4GB Interior storage:64GB Expandable storage:Yes Expandable capacity type:microSD Expandable capacity up to (GB):512 Committed microSD space: Yes Back camera:48-megapixel + 8-megapixel + 2-megapixel)+ 2-megapixel No. of Rear Cameras:4 Back autofocus:Yes Back Flash: Yes Front camera:13-megapixel No. of Front Cameras:1 Working framework: Android 11 Skin: MIUI 12 Finger impression sensor: Yes Compass/Magnetometer:Yes Nearness sensor: Yes Accelerometer: Yes Surrounding light sensor: Yes Spinner : Yes Experts Eye-getting plan. Great camera yield from the essential camera. Great presentation and incredible battery life. Cons Baffling gaming execution. Realme 8 : The Realme 8 is a decent gadget for media utilization with an alluring striking plan. experience splendid, distinctive shadings with a 6.4″ super AMOLED full showcase. A touch inspecting pace of 180Hz.The fast in-show unique mark scanner gives a simpler open encounter. It accompanies a 5000mAh battery viable with 30W Fast Charging innovation. Hey Res affirmed sound for a vivid sound experience.The super-flimsy 7.99mm and 177g design.6GB RAM with 128GB in-assembled capacity. The Neon Portrait highlights assist with featuring your magnificence. The Dynamic Bokeh highlights assist you with taking more jazzy and dynamic pictures. The front and back cameras assist you with exploiting your inventiveness. Quickly charge the gadget to 100% in only 65 minutes. By utilizing slant shift mode you can add smaller than normal impacts to your photographs to make them look adorable and excellent. Assuming you are searching for Smartphones under Rs.15,000, you can go for Realme 8. We should take a gander at some specialized components: Measurements (mm):160.60 x 73.90 x 7.99 Weight (g):177.00 Battery limit (mAh):5000 Quick charging: Proprietary Shadings: Cyber Black, Cyber Silver Screen size (inches):6.40 Touchscreen: Yes Resolution:1080×2400 pixels Processor octa-center Processor make: MediaTek Helio G95 RAM:8GB Inner storage:128GB Expandable capacity: Yes Expandable capacity type:microSD Back camera:64-megapixel + 8-megapixel + 2-megapixel + 2-megapixel No. of Rear Cameras:4 Back self-adjust: Yes Back Flash: Yes Front camera:16-megapixel No. of Front Cameras:1 Working framework: Android 11 Skin: Realme UI 2.0 Face open: Yes In-Display Fingerprint Sensor: Yes Compass/Magnetometer:Yes Closeness sensor: Yes Accelerometer: Yes Encompassing light sensor: Yes Gyrator : Yes Stars Cons Dependable execution Disillusioning camera experience 90Hz revive rate show Bloatware-perplexed UI Great battery life. Slow charging Realme Narzo 30: On the off chance that you are searching for Best Smartphones under Rs.15,000, look at this Realme Narzo 30. The Realme Narzo 30 is a recently dispatched cell phone with brilliant components. Realme is one of the quickest developing brand in the Indian market. Going to its particulars, the new gadget has a splendid 6.5″ presentation which can assist you with opening up a totally different skyline. The cell phone has a huge 5000mAh battery. The gadget accompanies a MediaTek Helio G-85 octa-center processor. Realme Narzo 30 displays 64GB that is further expandable up to 256GB utilizing a microSD card. It accompanies a 48 MP AI Triple Camera with a 16MP front camera. It offers availability alternatives like Mobile Hotspot, Bluetooth v5.0, A-GPS Glonass, WiFi 802.11, USB Type-C, USB Charging alongside help for 4G VoLTE organization. This presentation of this Realme Narzo 30 offers a smooth looking over experience. This Realme Narzo 30 components a race track-roused V-speed configuration to offer an exciting, restless look. The realme Narzo 30 has Android 11 OS, and it is smooth and easy to use. The Realme Narzo 30 is one of the Most amazing Smartphone under Rs.15,000. We should take a gander at some specialized provisions: Screen Size (In Inches):6.5 Show Technology :IPS LCD Screen Resolution (In Pixels):1080 x 2400 Pixel Density (Ppi):270 Invigorate Rate:90 Hz Camera Features:Triple Back Camera Megapixel:48 + 2 + 2 Front Camera Megapixel:16 Face Detection:Yes Hdr:Yes Battery Capacity (Mah):5000 Quick Charging Wattage:30 W Charging Type Port :Type-C Cpu:Mediatek Helio G95 Central processor Speed:2×2.05 GHz, 6×2.0 GHz Processor Cores:Octa Ram:4 GB Gpu:Mali-G76 MC4 Measurements (Lxbxh-In Mm):162.3 x 75.4 x 9.4 Weight (In Grams):192 Storage:64 GB Stars Extraordinary presentation to watch recordings. Respectable essential camera in daytime. Cons Helpless low-light camera execution. Samsung Galaxy F22: Samsung presents the Samsung universe F22 cell phone which is the Best Smartphone under Rs.15,000.if you are a moderate client like online media, observe a few recordings, and mess around for the sake of entertainment, then, at that point this telephone is intended for you. Keeping in see the mid-range level of passage Samsung has made its quality felt inside the majority. Eminent telephone with a heavenly look and very magnificent execution Samsung Galaxy F22 accompanies a 16.23cm(6.4″)sAMOLED vastness U showcase. Super AMOLED with HD very much designed which is satisfying to the eye for long viewing.Glam up your feed with a genuine 42MP Quad camera. Consistent performing various tasks, monstrous capacity, and force loaded with the MTK G80 processor.Scanner.Available in two cool shadings Denim dark, Denim blue. Samsung Galaxy F22 accompanies a 6000mAh battery so you can go a whole day without having to continually re-energize. Each photograph that you catch on this Samsung cosmic system F22 will be clear and reasonable. make your installment speedy and quick by utilizing Samsung pay smaller than usual. We should take a gander at some specialized components: Measurements (mm):159.90 x 74.00 x 9.30 Weight (g):203.00 Battery limit (mAh):6000 Screen size (inches):6.40 Touchscreen:Yes Resolution:720×1600 pixels Assurance type:Gorilla Glass Processor:octa-center Processor make:MediaTek Helio G80 RAM:4GB Inward storage:64GB Working system:Android 11 Back camera:48-megapixel 8-megapixel + 2-megapixel + 2-megapixel No. of Rear Cameras:4 Back autofocus:Yes front camera:13-megapixel No. of Front Cameras:1 Aces: 90 Hz Refresh Rate. Samsung Pay Mini. Up-to-date Design.Motorola Moto G30: Motorola Moto G30: Motorola has dispatched Moto G30 is one of the Most outstanding Smartphones under Rs.15,000 in India. The cell phone has Android 11 OS with a close stock interface. Moto G30 accompanies a quad-camera which incorporates a 64MP essential sensor and 13 MP camera at the front. Moto G30 has two distinct shadings Dark Pearl and Pastel Sky tones. Moto G30 accompanies a 6.5-inch HD show with a 20:9 angle ratio,90Hz revive rate, and 720*1600 pixels show goal. The Moto G30 runs on Android 11. The telephone is stacked with highlights like Night Vision, shot advancement, Auto grin catch, HDR, and RAW photograph output.it is controlled by a Qualcomm Snapdragon 662 octa-center processor alongside 4 GB of RAM.it accompanies 64 GB of installed stockpiling that is expandable up to 512GB by means of a microSD card. Moto G30 has a 5,000mAh battery that can go more than 2 days on a solitary charge. Far reaching equipment and programming security ensure your own information is better ensured. By utilizing NFC innovation assists you with making smooth, quick, and secure installments when you hold it close to a NFC terminal.Connectivity choice incorporate Wi-Fi 802.11 a/b/g/n/ac, GPS, Bluetooth v5.00, NFC, and USB Type-C.It has measurements 169.60 x 75.90 x 9.80mm and weighs 225.00 g. We should take a gander at some specialized components: Manufacturer:Moto Model:G30 Dispatch Date (Global):09-03-2021 Working System:Android Operating system Version:11 Display:6.50-inch, 720×1600 pixels Processor:Qualcomm Snapdragon 662 RAM:4GB Battery Capacity:5000mAh Back Camera: 64MP + 8MP +2MP Front Camera:13MP Computer chip Speed:4×2.0 GHz, 4×1.8 GHz Processor Cores:Octa-center Gpu:Adreno 610 Measurements (Lxbxh-In Mm) :165.2 x 75.7 x 9.1 Weight (In Grams) :200 Storage:128 GB Quick Charging Wattage:20W Charging Type Port:Type-C Experts: High invigorate rate show Clean Android 11 UI Great battery execution Good cameras Cons: Huge and cumbersome Forceful Night mode. 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