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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh Mohammed Salahat Taher M.Ghazal Mohsen A.A.Tomh beenu mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas beenu mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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Celiac disease:Overview and considerations for development of gluten-free foods

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Food Science and Human Wellness 2016年第4期5卷 169-176页

作者： Prakriti Jnawali Vikas Kumar beenu Tanwar Food Technology and Nutrition School of AgricultureLovely Professional UniversityPhagwaraPunjab 144411India

Celiac disease is a genetically-determined chronic inflammatory intestinal disease induced by gluten in wheat,barley,rye etc.Celiac disease affects approximately one percent of people in the world and strict gluten-free diet(GFD)for a lifetime is the only available treatment.As gluten-free products available in the market are known to have low nutritional quality as well as are more expensive than gluten-containing food products,there is a strong need to develop gluten-free products that are nutritionally complete as well as economical.This review focuses on the special considerations during developing gluten-free products viz.,finding an alternate non-gluten source,ensuring nutrition and sensory quality characteristics,compliance with the regulatory guidelines,economics and product.

关键词： Celiac disease Gluten Gluten-free diet Functional foods Sensory properties

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Anti-HBc screening in Indian blood donors:Still an unresolved issue

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World Journal of Gastroenterology 2008年第34期14卷 5327-5330页

作者： Hari Krishan Dhawan Neelam Marwaha Ratti Ram Sharma Yogesh Chawla beenu Thakral Karan Saluja Sanjeev Kumar Sharma Manish K Thakur Ashish Jain Department of Transfusion Medicine PGIMER Department of Hepatology PGIMER

AIM:To study the seroprevalence of antibody to hepatitis B core antigen (anti-HBc) in healthy blood donors negative for HBsAg and to evaluate whether anti-HBc detection could be adopted in India as a screening assay for HBV in addition to HBsAg. METHODS: A total of 1700 serum samples collected from HBsAg-negative healthy blood donors were tested for the presence of anti-HBc antibody (IgM + IgG). All samples reactive for anti-HBc antibody were then investigated for presence of anti-HBs and for liver function tests (LFTs). One hundred serum samples reactive for anti-HBc were tested for HBV DNA by PCR method. RESULTS: Out of 1700 samples tested, 142 (8.4%) blood samples were found to be reactive for anti-HBc. It was signif icantly lower in voluntary (6.9%) as compared to replacement donors (10.4%, P = 0.011). Seventy- two (50.7%) anti-HBc reactive samples were also reactive for anti-HBs with levels > 10 mIU/mL and 70 (49.3%) samples were non-reactive for anti-HBs, these units were labeled as anti-HBc-only. These 142 anti-HBc reactive units were also tested for liver function test. HBV DNA was detected in only 1 of 100 samples tested. CONCLUSION: Keeping in view that 8%-18% of donor population in India is anti-HBc reactive, inclusion of anti- HBc testing will lead to high discard rate. Anti-HBs as proposed previously does not seem to predict clearance of the virus. Cost effectiveness of introducing universalanti-HBc screening and discarding large number of blood units versus considering ID NAT (Individual donor nuclic acid testing) needs to be assessed.

关键词：肝炎乙肝表面抗原印度献血人群

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