Survey of Eye-Free Text Entry Techniques of Touch Screen Mobile Devices Designed for Visually Impaired Users

Now a days touch screen mobiles are becoming more popular amongst sighted as well visually impaired people due to its simple interface and efficient interaction techniques. Most of the touch screen devices designed for visually impaired users based on screen readers, haptic and different user interface (UI).In this paper we present a critical review of different keypad layouts designed for visually impaired users and their effect on text entry speed. And try to list out key issues to extend accessibility and text entry rate of touch screen devices.Keywords: Text entry rate, touch screen mobile devices, visually impaired users

Analysis of Modified Rule Extraction Algorithm and Internal Representation of Neural Network

Classification and Rule extraction is an important application of Artificial Neural Network. To extract fewer rules from multilayer feed forward neural network has been a research area. The internal representation of the network is augmented by a distance term to extract fewer rules from the feedforward neural network and experimented on five datasets. Understanding affect of different factors of the dataset and network on extraction of a number of rules from the network can reveal important pieces of information which may help researchers to enhance the rule extraction process. This work investigates the internal behavior of neural network in rule extraction process on five different dataset.Keywords: Rule extraction, Feed Forward Neural Network, Hidden units, Activation value, Hidden neurons

Analysis of Modified Rule Extraction Algorithm and Internal Representation of Neural Network

Classification and Rule extraction is an important application of Artificial Neural Network. To extract fewer rules from multilayer feed forward neural network has been a research area. The internal representation of the network is augmented by a distance term to extract fewer rules from the feedforward neural network and experimented on five datasets. Understanding affect of different factors of the dataset and network on extraction of a number of rules from the network can reveal important pieces of information which may help researchers to enhance the rule extraction process. This work investigates the internal behavior of neural network in rule extraction process on five different dataset.Keywords: Rule extraction, Feed Forward Neural Network, Hidden units, Activation value, Hidden neurons

Survey of Eye-Free Text Entry Techniques of Touch Screen Mobile Devices Designed for Visually Impaired Users

Now a days touch screen mobiles are becoming more popular amongst sighted as well visually impaired people due to its simple interface and efficient interaction techniques. Most of the touch screen devices designed for visually impaired users based on screen readers, haptic and different user interface (UI).In this paper we present a critical review of different keypad layouts designed for visually impaired users and their effect on text entry speed. And try to list out key issues to extend accessibility and text entry rate of touch screen devices.Keywords: Text entry rate, touch screen mobile devices, visually impaired users

Power spectrum: A detailed dataset on electric demand and environmental interplays

This dataset provides detailed electricity demand forecasting metrics for the Sharjah Electricity and Water Authority (SEWA) over 2020 and 2021. Data encompasses both hourly and daily demand patterns, enriched with specific environmental parameters such as temperature, humidity, and solar irradiance. Additionally, SEWA's unique load metrics and lagged demand values, representing previous hour demand, are included.Data was procured using advanced electrical load meters and standardized weather data acquisition systems. Preliminary and advanced data processing was conducted via Excel tool. This comprehensive dataset is invaluable for stakeholders in electricity provisioning and policy-making. Its granular detail makes it a pivotal resource for modelling and forecasting electricity demand, aiding in infrastructure planning, renewable energy considerations, and demand-side management. The potential applications span across academic, policy, and industry domains, rendering it a versatile tool for future electricity demand research

Development of An IoT-Enabled Photovoltaic-Battery Renewable Energy System

Solar energy is considered as a prominent source of renewable energy, mainly due to the vast abundance of sunlight and rapid advancements of photovoltaic (PV) technology. The performance, reliability and lifespan of PV systems are severely affected by numerous environmental factors and fault occurrences, which include: (1) extreme swing in the operating temperature; (2) low solar irradiation levels which appear undetected in PV systems, resulting in energy losses and degradation of PV panels; and (3) non-homogenous shading and accumulation of dirt on PV panels, causing thermal imbalance and hotspots on the panels. Therefore, it is important to monitor the operating temperature and homogeneous detection of sunlight on the PV modules to guarantee efficient energy production. In this paper, we present the development and demonstration of a sensor-assisted Internet of Things (IoT)-based photovoltaic-battery renewable energy system. The adoption of the IoT solution for monitoring the real-time variations in environmental factors and system performance is discussed here. For the PV-battery hardware module, solar panels along with rechargeable batteries are constructed to supply the system. Inverters and controllers are used to synchronize the voltage level and transformation of AC power from DC power. In the design of the IoT system, the Arduino Mega microcontroller and ESP32 TTGO board are used along with sensors for recording the temperature, presence of dust/dirt, and voltage and current levels. The working prototype enables real-time data to be captured and sent to the cloud database for data collection, performance analysis, and diagnosis/detection of faults in the proposed system

A nomogram-based diabetic sensorimotor polyneuropathy severity prediction using Michigan neuropathy screening instrumentations

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is one of the major indelible complications in diabetic patients. Michigan neuropathy screening instrumentation (MNSI) is one of the most common screening techniques used for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features. Results: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively. Conclusions: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients. 2021This research is financially supported by Xiamen University Malaysia, Project number XMUMRF/2018-C2/IECE/0002; Universiti Kebangsaan Malaysia (UKM), Grant Number DPK-2021-001, GP-2020-K017701 and MI-2020-002 and Qatar National Research Foundation (QNRF), grant no. NPRP12s-0227-190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021-001.We would like to thank the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for providing the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) and Diabetes Prevention Program Outcomes Study (DPPOS) database. The Diabetes Control and Complications Trial (DCCT) and its follow-up the Epidemiology of Diabetes Interventions and Complications (EDIC) study were conducted by the DCCT/EDIC Research Group and supported by National Institute of Health grants and contracts and by the General Clinical Research Center Program, NCRR. The Diabetes Prevention Program outcome study (DPPOS) was conducted by the DPP Research Group and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the General Clinical Research Center Program, the National Institute of Child Health and Human Development (NICHD), the National Institute on Aging (NIA), the Office of Research on Women's Health, the Office of Research on Minority Health, the Centers for Disease Control and Prevention (CDC), and the American Diabetes Association. The data [and samples] from the DCCT/EDIC and DPPOS study were supplied by the NIDDK Central Repositories. This manuscript was not prepared under the auspices of the DCCT/EDIC and DPPOS and does not represent analyses or conclusions of the DPP Research Group, the NIDDK Central Repositories, or the NIH. The database is available on request from the NIDDK websites. EDIC database:, https://repository.niddk.nih.gov/studies/edic/and DPPOS database: https://repository.niddk.nih.gov/studies/dppos/Scopu