Error Rate Reduction Research Articles

Design of DNA Storage Coding with Enhanced Constraints.

Traditional storage media have been gradually unable to meet the needs of data storage around the world, and one solution to this problem is DNA storage. However, it is easy to make errors in the subsequent sequencing reading process of DNA storage coding. To reduces error rates, a method to enhance the robustness of the DNA storage coding set is proposed. Firstly, to reduce the likelihood of secondary structure in DNA coding sets, a repeat tandem sequence constraint is proposed. An improved DTW distance constraint is proposed to address the issue that the traditional distance constraint cannot accurately evaluate non-specific hybridization between DNA sequences. Secondly, an algorithm that combines random opposition-based learning and eddy jump strategy with Aquila Optimizer (AO) is proposed in this paper, which is called ROEAO. Finally, the ROEAO algorithm is used to construct the coding sets with traditional constraints and enhanced constraints, respectively. The quality of the two coding sets is evaluated by the test of the number of issuing card structures and the temperature stability of melting; the data show that the coding set constructed with ROEAO under enhanced constraints can obtain a larger lower bound while improving the coding quality.

Improved DV-Hop Algorithm Based on Swarm Intelligence for AI and IoT-Federated Applications in Industry 4.0

In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm’s hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles’ velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles’ movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0.

Nanopore Sequencing for Characterization of HIV-1 Recombinant Forms.

ABSTRACTHigh genetic diversity, including the emergence of recombinant forms (RFs), is one of the most prominent features of human immunodeficiency virus type 1 (HIV-1). Conventional detection of HIV-1 RFs requires pretreatments, i.e., cloning or single-genome amplification, to distinguish them from dual- or multiple-infection variants. However, these processes are time-consuming and labor-intensive. Here, we constructed a new nanopore sequencing-based platform that enables us to obtain distinctive genetic information for intersubtype RFs and dual-infection HIV-1 variants by using amplicons of HIV-1 near-full-length genomes or two overlapping half-length genome fragments. Repeated benchmark tests of HIV-1 proviral DNA revealed consensus sequence inference with a reduced error rate, allowing us to obtain sufficiently accurate sequence data. In addition, we applied the platform for sequence analyses of 9 clinical samples with suspected HIV-1 RF infection or dual infection according to Sanger sequencing-based genotyping tests for HIV-1 drug resistance. For each RF infection case, replicated analyses involving our nanopore sequencing-based platform consistently produced long consecutive analogous consensus sequences with mosaic genomic structures consisting of two different subtypes. In contrast, we detected multiple heterologous sequences in each dual-infection case. These results demonstrate that our new nanopore sequencing platform is applicable to identify the full-length HIV-1 genome structure of intersubtype RFs as well as dual-infection heterologous HIV-1. Since the genetic diversity of HIV-1 continues to gradually increase, this system will help accelerate full-length genome analysis and molecular epidemiological surveillance for HIV-1.IMPORTANCE HIV-1 is characterized by large genetic differences, including HIV-1 recombinant forms (RFs). Conventional genetic analyses require time-consuming pretreatments, i.e., cloning or single-genome amplification, to distinguish RFs from dual- or multiple-infection cases. In this study, we developed a new analytical system for HIV-1 sequence data obtained by nanopore sequencing. The error rate of this method was reduced to ~0.06%. We applied this system for sequence analyses of 9 clinical samples with suspected HIV-1 RF infection or dual infection, which were extracted from 373 cases of HIV patients based on our retrospective analysis of HIV-1 drug resistance genotyping test results. We found that our new nanopore sequencing platform is applicable to identify the full-length HIV-1 genome structure of intersubtype RFs as well as dual-infection heterologous HIV-1. Our protocol will be useful for epidemiological surveillance to examine HIV-1 transmission as well as for genotypic tests of HIV-1 drug resistance in clinical settings.

EVALUATING THE IMPACT OF ELECTRONIC HEALTH RECORD SYSTEMS ON REDUCING MEDICATION ERRORS: A STUDY AT LADY READING HOSPITAL, PESHAWAR

Medication mistakes significantly threaten hospitals, leading to adverse events and increased healthcare costs. Electronic health records (EHRs) have been introduced to reduce the frequency of errors and mitigate these issues. This study aimed to determine if the EHR system reduced medical errors at Lady Reading Hospital (LRH) in Peshawar, Pakistan, thereby enhancing patient safety. A retrospective study was conducted from January 3, 2021, to July 3, 2021. The assessment involved a detailed analysis of medication error incidents at LRH, recorded during this period, involving 200 patients. Data from the Department of Pharmacy were collected and analyzed concerning the occurrence, types, and possible contributing causes of medication errors. The results showed a significant reduction in medication error rates following the implementation of the EHR system. The number of medication errors decreased from 80 incidents to 30 occurrences. Specifically, overall mistakes dropped from 25 to 5, omissions from 15 to 10, and wrong medication reports from 20 to 8. The most common types of errors included dosage errors (accounting for 50 incidents), followed by wrong medication (30 incidents), and omissions (25 incidents). Factors contributing to these errors included communication gaps (20 incidents), transcribing errors (15 incidents), and inadequate training (10 incidents). An evaluation of the severity of these errors revealed 15 minor, 25 moderate, and 10 severe problems. Implementing the EHR system significantly enhanced patient safety by improving communication and providing continuous access to accurate patient information. The findings indicate that EHRs are a crucial component of hospital drug safety, significantly reducing medication errors. Integrating electronic record-keeping facilitates medication management processes, provides real-time information access, and improves communication among healthcare professionals. However, addressing other aspects of system malfunctions and ensuring effective system distribution is essential for successful EHR implementation in healthcare facilities. Future research should focus on designing longitudinal studies to evaluate the long-term safety of medications, the use of EHR systems, and strategies to further improve patient care quality.

Identification of crucial geometric errors of face gear grinding machine based on geometric error-tooth surface normal error model considering worm dressing

The existence of geometric error (GE) of the grinding machine leads to a great decrease in the accuracy of worm dressing and face gear grinding. Currently, scarce researches consider the influence of GE of the grinding machine on the tooth surface normal error (TSNE) of face gears in the whole process from worm dressing to face gear grinding. For straightly disclosing the mapping laws between the GE and TSNE, the whole process from worm dressing to face gear grinding, and the interaction between the worm and face gear tooth surface are simultaneously considered to establish the GE-TSNE model (GE-TSNEM). Then, the crucial GEs are identified through global sensitivity analysis combined with actual GE measurement results. Finally, the validity of the identification results is verified. The results show that the crucial GEs in the whole dressing-grinding process can be effectively identified through one calculation by the GE-TSNEM. More than 25% of the crucial GEs originate from the worm dressing process, and the consideration of the worm dressing process is indispensable when establishing the GE-TSNEM. The correctness of the identification results and the validity of the method are verified as the total error reduction rates are higher than 75%.

Reliability-Based Large-Vocabulary Audio-Visual Speech Recognition.

Audio-visual speech recognition (AVSR) can significantly improve performance over audio-only recognition for small or medium vocabularies. However, current AVSR, whether hybrid or end-to-end (E2E), still does not appear to make optimal use of this secondary information stream as the performance is still clearly diminished in noisy conditions for large-vocabulary systems. We, therefore, propose a new fusion architecture—the decision fusion net (DFN). A broad range of time-variant reliability measures are used as an auxiliary input to improve performance. The DFN is used in both hybrid and E2E models. Our experiments on two large-vocabulary datasets, the Lip Reading Sentences 2 and 3 (LRS2 and LRS3) corpora, show highly significant improvements in performance over previous AVSR systems for large-vocabulary datasets. The hybrid model with the proposed DFN integration component even outperforms oracle dynamic stream-weighting, which is considered to be the theoretical upper bound for conventional dynamic stream-weighting approaches. Compared to the hybrid audio-only model, the proposed DFN achieves a relative word-error-rate reduction of 51% on average, while the E2E-DFN model, with its more competitive audio-only baseline system, achieves a relative word error rate reduction of 43%, both showing the efficacy of our proposed fusion architecture.

Military Target Detection in Remote Sensing Imagery Based on YOLOv4-Faster

The continuing development of remote sensing has resulted in a rapidly increasing number of remote sensing applications. High-resolution remote sensing images are used in various fields in the military. We propose methods for object detection based on remote sensing images. We develop a signal processing method for normalizing remote sensing images to eliminate noise such as fog, haze, and poor lighting. This method further improves detection accuracy and reduces error rates. We develop YOLOv4-faster, an accelerated neural network model based on the YOLO (You-Only-Look-Once) object detection method. YOLOv4-faster outperforms existing networks in terms of execution time and detection performance. We conduct a series of experiments on two public datasets (TGRS-HRRSD and NWPU VHR-10) as well as a dataset containing six military target classes provided by IMINT & Analysis and collected from Google Earth. YOLOv4-faster improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.

Radiology Quality Improvement: Lessons From the “Toyota Way”

A medical student applying for radiology residency was formerly employed by Toyota as an engineer. During that time, he learned that production-line employees found it difficult to install moonroofs, experiencing back pain after climbing into and out of trucks 120 times per day. He walked the production line and talked with team members, brainstorming process improvement ideas and developing a solution, which he reviewed and reflected on each day. Within months, his team rectified the ergonomic problems and designed and installed an in-line quality assessment device that improved workers’ experiences and saved the company over $8 million per year by reducing error rates.

Computed tomographic evaluation of the acetabulum for age estimation in an Indian population using principal component analysis and regression models

The acetabulum presents as a well-preserved evidence, resistant to taphonomic degradation changes and can thus aid in the age estimation process. A CT-based examination of the acetabulum can further help simplify the process of age estimation by overcoming the time-consuming process of maceration and by doing away with the interference resulting from tissue remnants. The aim of the present study was to evaluate the role of the acetabulum for age estimation in an Indian population through a CT-based examination, using principal component analysis and regression models. CT images of 400 individuals aged 10years and above were evaluated according to the features defined in the San-Millán-Rissech method of age estimation. Five of the seven morphological features defined by San-Millán-Rissech were appreciable on CT scans, and, to enable further statistical analysis, a cumulative score was computed using these five features. A significant correlation of 0.835 and 0.830 for the right and left acetabulum, respectively, was obtained between computed cumulative scores and chronological age of individuals. No significant sex differences were observed in the scoring of different age-related morphological changes. Regression models were generated using individual features and cumulative scores. Regression models derived using the cumulative score yielded inaccuracy values of 9.67years for the right acetabulum and 9.15years for the left acetabulum. Inaccuracy and bias values were computed for each individual feature, as well as for each decade, using mean point ages established within the original study. Amongst the various features, acetabular rim porosity was seen to have the lowest values of inaccuracy (11.50years) and bias (2.32years) and activity on outer edge of acetabular fossa the highest (inaccuracy and bias values of 22.36years and 21.50years, respectively). Taking into consideration this differential contribution towards age estimation, weighted coefficients and mean point ages for different morphological features were determined using principal component analysis. Subsequently, summary age models were generated from the obtained weighted coefficients and mean age values. Summary age models derived in the present study yield lower estimates of inaccuracy of 7.60years for the right acetabulum and 7.82years for the left acetabulum. While regression models derived in the present study allow for age estimation using even a single appreciable feature, summary age models take into account the contribution of each feature and generate more accurate estimates of age. Both statistical computations yield reduced error rates and thus can render greater applicability to the acetabulum in forensic age estimation.

Interactive PDF Forms to Conduct Online Examinations in University Education: Practical Experience and Lessons Learned

Due to the Corona Pandemic, paper-based examinations had to be transferred to an online format on short notice. At the Lucerne University of Applied Sciences and Arts we conducted three online examinations with a total of 816 students using an interactive PDF form. We faced two major challenges redesigning our examinations: 1) avoiding unnecessary stress to students' working memory by choosing a simple examination design and 2) minimizing the possibility of cheating. In our paper we explain how we addressed these challenges working with an interactive PDF form in combination with the learning management system Ilias. Our results show that using an interactive PDF form lowers the extraneous cognitive load on our students, increases our efficiency in correcting, reduces error rates in grading and further allows for faster feedback to our students.

Interface software can markedly reduce time and improve accuracy for clinical trial data transfer from EMR to EDC: The results of two measure of work time studies comparing commercially available clinical data transfer software to current practice manual data transfer.

1573 Background: Clinical studies and new drug approvals are delayed by slow data transfer and transcription errors from site entered data. These delays have been compounded by a shortage of data entry personnel at sites such that data entry approaches 10-30 days post visit. Data transfer is largely performed by manual keyboard entry from electronic medical records (EMR) into electronic case report forms (ECRF). Methods: We conducted two separate measure-of-work time studies to compare a commercially available interface software product, ProXimity to the current manual data entry. The clinical trial data from two different EMRs (ARIA and IKM G2) to an EDC (Medidata Rave). The EDC mirrored an IRB approved clinical study. Time to transfer data and error rates were the primary and secondary endpoints, respectively. For study 1 Aria EMR data from 3 subjects and 1497 data fields including demographics, vital signs, ECOG PS, physical findings, adverse events, and lab results including CBC, CMP, urinalysis, coagulation, serology were selected for visits from Screening and C2D1. For Study 2 IKM G2 data from 6 subjects and 834 data fields included demographics, vitals, and lab results. The data entry personnel were aware of the timed nature of the study. Results: Study 1 ProXimity took 13.2 min to transfer the data compared to 73.4 min for manual entry, with error rates of 0.8% compared to 3.5%, respectively. In Study 2 Proximity took 6.5 min compared to 29 min for manual entry, with error rates of 1.4% each due to non- conformant data (text). Conclusions: Software data transfer interfaces can markedly shorten the time for data entry, reduce error rates and reduce operational costs.

PAPR reduction technique for FBMC based visible light communication systems

One of the critical challenges in multicarrier-based systems is peak to average power ratio (PAPR). Recently, Filter Bank Multicarrier (FBMC) is proved to be a promising candidate that can replace the traditional orthogonal multiplexing division (OFDM) scheme due to its better spectral efficiency, reducing both inter-channel interference (ICI) and PAPR. Due to their advantages in reducing the PAPR without impacting the BER, the Hadamard transform was used in the proposed FBMC based VLC system. Furthermore, here, the discreet cosine transform (DCT) precoding is also used to boost the potential for PAPR reduction and the BER efficiency. The negative signals are not clipped off as in traditional asymmetrically-clipped optical OFDM (ACO-OFDM) signals to reduce the impact of large-amplitude signal reduction nor add dc biasing for cancelling negative signals as in traditional DC-biased optical OFDM (DCO-OFDM) signals. Furthermore, a Clipping ratio is introduced to allow a trade-off between bit error rate (BER) and PAPR reduction, and the optimal PAPR reduction is investigated. The obtained results show that the proposed FBMC based VLC system with DCT and clipping technique can reduce the PAPR and achieve good BER efficiency compared to the OFDM based VLC system.

Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits.

Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting

AbstractEnsuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia’s congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority-minority districts during the redistricting process.

Low-resource automatic speech recognition and error analyses of oral cancer speech

In this paper, we introduce a new corpus of oral cancer speech and present our study on the automatic recognition and analysis of oral cancer speech. A two-hour English oral cancer speech dataset is collected from YouTube. Formulated as a low-resource oral cancer ASR task, we investigate three acoustic modelling approaches that previously have worked well with low-resource scenarios using two different architectures; a hybrid architecture and a transformer-based end-to-end (E2E) model: (1) a retraining approach; (2) a speaker adaptation approach; and (3) a disentangled representation learning approach (only using the hybrid architecture). The approaches achieve a (1) 4.7% (hybrid) and 7.5% (E2E); (2) 7.7%; and (3) 2.0% absolute word error rate reduction, respectively, compared to a baseline system which is not trained on oral cancer speech. A detailed analysis of the speech recognition results shows that (1) plosives and certain vowels are the most difficult sounds to recognise in oral cancer speech — this problem is successfully alleviated by our proposed approaches; (3) however these sounds are also relatively poorly recognised in the case of healthy speech with the exception of/p/. (2) recognition performance of certain phonemes is strongly data-dependent; (4) In terms of the manner of articulation, E2E performs better with the exception of vowels — however, vowels have a large contribution to overall performance. As for the place of articulation, vowels, labiodentals, dentals and glottals are better captured by hybrid models, E2E is better on bilabial, alveolar, postalveolar, palatal and velar information. (5) Finally, our analysis provides some guidelines for selecting words that can be used as voice commands for ASR systems for oral cancer speakers.

Performance Analysis of Total Attenuation Effects and Different Values of Transmitter Power on Bit Error Rate and Signal-To-Noise Ratio for Free Space Optical Communication

Objective: In this paper, the performance of pulse position modulation (PPM), nonreturn zero modulation (NRZ), and return zero modulation (RZ) at signal-to-noise ratio and bit error rate in free space optical communication is compared. This comparison is performed to obtain the most effective modulation in atmospheric attenuations. Also, the effect of increasing transmitter power on the bit error rate and signal-to-noise ratio is investigated. Methods: Utilizing a light source with a wavelength of 1550 nm, the system is simulated in MATLAB by choosing the Kim data transmission link model with different visibility values of 0- 10 km. The analytical equations of free space optical communications are implemented by selecting appropriate parameters. The effects of weak, moderate, and strong atmospheric attenuation, geometric loss, and different transmitter powers (1-5 mW) on bit error rate and signal-to-noise ratio are investigated for all three modulations. Results: The results show that PPM is the most effective modulation compared to other modulations used in this study and shows a better performance in the mentioned atmospheric attenuation conditions within the given values of bit error rate and signal-to-noise ratio. Conclusion: Modulations with lower average power will perform better than other modulations in bit error rate and signal-to-noise ratio under the same atmospheric attenuation conditions. In PPM modulation, increasing the transmitter power causes more reduction in the bit error rate than other modulations used in this paper. Therefore, using this modulation to optimize the power budget in free space optical communications will be appropriate.

Event Classification and Intensity Discrimination for Forest Fire Inference With IoT

Simultaneously occurring random events are often reported by multiple nodes. However, the accuracy of the event detection at every node is dependent on the node’s relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzyfied values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. Extensive analysis of the results proves the efficacy of the proposed scheme with a significantly reduced error rate of 2.01% for humidity and an error rate of 1.94% for temperature.

Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology

Deep myxoid soft tissue lesions have posed a diagnostic challenge for pathologists due to significant histological overlap and regional heterogeneity, especially when dealing with small biopsies which have profoundly low accuracy. However, accurate diagnosis is important owing to difference in biological behaviors and response to adjuvant therapy, that will guide the extent of surgery and the need for neo-adjuvant therapy. Herein, we trained two convolutional neural network models based on a total of 149,130 images representing diagnoses of extra skeletal myxoid chondrosarcoma, intramuscular myxoma, low-grade fibromyxoid sarcoma, myxofibrosarcoma and myxoid liposarcoma. Both AI models outperformed all the pathologists, with a significant improvement of accuracy up to 97% compared to average pathologists of 69.7% (p < 0.00001), corresponding to 90% reduction in error rate. The area under curve of the best AI model was on average 0.9976. It could assist pathologists in clinical practice for accurate diagnosis of deep soft tissue myxoid lesions, and guide clinicians for precise and optimal treatment for patients.

Design and development of smart Internet of Things-based solid waste management system using computer vision.

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision–based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.

Automatic Classification of Normal and Infected Blood Cells for Leukemia Through Color Based Segmentation Technique Over Innovative CNN Algorithm and Comparing the Error Rate with RNN

To classify the normal infected blood cells through color-based segmentation for leukemia by comparing the error rate for the innovative Convolutional Neural Network and Recurrent Neural Network algorithm. Materials and Methods: Convolutional Neural Network algorithm, which has been taken as an input image and differentiating according to the properties of the image. Here the white blood cells acted as the major parameter for detecting the disease. Result: Data collection was carried out and the analysis could have been done by using blood cell sample images to detect the result and error rate of a particular algorithm. Here in this proposed work, the error rate was reduced in innovative Convolutional Neural Networks compared to Recurrent Neural Networks. Conclusion: The data was collected from various resources for the usage of disease detection. The reduced error rate for the Convolutional Neural Network (87.02%) was used as an algorithm for the whole disease detection process for reduced error rate results compared to the Recurrent Neural Network (89.42%).