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The rise of the internet and social media has facilitated diverse interactions among individuals, but it has also led to an increase in cyberbullying—a phenomenon with detrimental effects on mental health, including the potential to induce suicidal thoughts. To combat this issue, we have developed the Cyberbullying Bengali Dataset (CBD), a novel resource containing 2751 manually labeled texts categorized into five classes, including various forms of cyberbullying and non-bullying instances. In our study on cyberbullying detection, we conducted an extensive evaluation of various machine learning and deep learning models. Specifically, we examined Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Random Forest (RF) among the traditional machine learning models. For deep learning models, we explored Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). We have also experimented with state-of-the-art transformer architectures, including m-BERT, BanglaBERT, and XLM-RoBERTa. After rigorous experimentation, XLM-RoBERTa emerged as the most effective model, achieving a significant F1-score of 0.83 and an accuracy of 82.61%, outperforming all other models. Our work provides insights into effective cyberbullying detection on platforms like Facebook, YouTube, and Instagram.

The machine learning (ML) model provides an alternative method for estimating inherent optical properties (IOPs) in clear and coastal waters. This study introduces an effective approach by employing ensemble machine learning techniques, such as random forest, gradient boosting, extra tree, adaboost, bagging, and voting model, to predict phytoplankton absorption coefficient ( $\text{a}_{\mathrm {ph}}(\lambda)$ , $\text{m}^{-1}$ ) at selected key wavebands of 443, 489, 510, 555, and 670 nm in clear and coastal waters. The optimization of the hyperparameters of these models through Bayesian techniques ensured high predictive accuracy. Furthermore, this research highlights the critical importance of wavelengths 670, 489, and 510 nm through feature importance analysis. The models exhibit excellent performance in terms of the coefficient of determination (R2) value when predicting phytoplankton at various wavelengths (e.g., 443, 489, 510, 555, and 670 nm). The R2 value of around 0.9033 is obtained for the absorption coefficient of phytoplankton aph at the wavelength 510 nm. The lowest mean squared error (MSE) of 0.0001 was achieved at the green waveband (i.e., 555 nm). Other statistical matrices, such as mean absolute percentage error (MAPE) and mean absolute error (MAE), have shown a low error across the selected wavelengths. It is found that the predicted phytoplankton absorption coefficients are in close agreement with actual values. This study shows the success of optimized ensemble models for both global and selected regional datasets that can accurately derive $\text{a}_{\mathrm {ph}}(\lambda)$ , which will contribute to the improvement of ocean primary productivity modelling and understanding the distribution of phytoplankton blooms.

The increase in renewable energy resources penetration into microgrids weakens the inertia of microgrids and negatively affects their frequency stability. This reduction in inertia reduces the microgrid’s ability to handle disturbances and increases frequency instability risk. The purpose of this paper is to present a virtual inertia (VI) control strategy for an island microgrid with the use of an energy storage system (ESS) to improve inertia and frequency stability of the system. The VI controller parameters are optimized using the differential evolution (DE) algorithm. The proposed VI control strategy can effectively improve microgrid frequency stability and reduce the risk of frequency instability. The results demonstrate that the proposed strategy is effective and can provide fast and reliable virtual inertia support for the microgrid. The proposed VI control approach is verified through simulation results and performs better than the conventional inertial response for frequency stability in islanded microgrids. Virtual inertia control injects/absorbs active power into/from the microgrid during frequency deviations. Different disturbances are applied to demonstrate uncertain load and RES generation behavior. The proposed VI control method is compared with the available literature. In the proposed method, the frequency deviation is better controlled. Furthermore, the results demonstrate a more stable and efficient system operation with the developed VI controller than the existing methods.

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

The main objective of this study was to investigate the vulnerability to climate change in the drought-prone region of Bangladesh through the utilization of a social-ecological system framework. The research was conducted in three sub-districts of Bangladesh prone to drought: Atrai, Nachole, and Tanore. The entropy method used exposure, sensitivity, and adaptive capacity indicators to estimate vulnerability. The data was obtained by administering a structured questionnaire to 100 households and gathering meteorological information. The study’s findings reveal that the Barind region, namely Atrai, Nachol, and Tanore, exhibit a high level of sensitivity to climate change, as evidenced by their respective sensitivity indices of 0.465, 0.47, and 0.507. Additionally, the region demonstrates moderate adaptive potential, as evidenced by their corresponding indices. Specifically, Atrai, Nachol, and Tanore possess scores of 0.577, -0.48, and -0.191, respectively. The study areas exhibit high drought severity exposure, as evidenced by the values of Atrai-0.555, Nachol-0.595, and Tanore-0.551. Moreover, the study indicates that the capacity to handle drought is moderately vulnerable to climate change and requires economic cooperation to prepare for future conditions. Key initiatives include providing financial support for smallholder farmers, enforcing agricultural policies, investing in social capital, and implementing government-backed infrastructure and technology enhancements, all crucial for building resilience against climate change. The research findings can provide a foundation for making climate adaptation decisions in the Barind region and other comparable locations across the globe.

The developments in engineering fields have extended the use of electromyography (EMG) beyond traditional diagnostic applications to multifarious areas like movement analysis. Surface EMG-based gesture recognition systems can provide the instinctive and exact recognition of various gestures with an effective classifier. Many researches have been done intensively on recognizing wrist and whole-hand gestures. Still, very little research was done on individual finger gestures, as this is considered more challenging due to the complexity and subtleness of muscle for individual finger movements. In this study, we tried to review surface EMG-based hand/finger movement recognition techniques using machine learning classifiers. We selected and evaluated 33 primary documents for this systematic literature review according to the Kitchenham methodology. Based on the research questions, this paper analyzes machine learning algorithms’ applicability, accuracy, and efficiency. It reviews the basic hand/finger gesture recognition techniques using a standard model. We also tried to identify the trends and gaps in the studied articles that could lead to new areas of study in the future.

This study presents a modular Fuzzy Expert System (FES) for chemotherapy drug dose scheduling. The computational model considers the patient’s Body Surface Area (BSA) and experts’ opinions to calculate chemo doses following the clinical practice. A proper balance between reducing cancerous cells and toxic side effects is required for effective drug scheduling. Still, in many cases, traditional clinical approaches fail to determine appropriate therapeutical doses that balance all restrictions. In our proposed system, FES-1 is developed to determine primary drug doses based on experts’ opinions and competing treatment objectives. To adjust the dose, FES-2 is developed based on clinical practices, the patient’s BSA, and experts’ opinions. The final chemotherapy drug dose schedule is generated by combining the outputs of FES-1 and FES-2, which is the proposed modular FES. A growth model is used in this work to observe response due to administration of chemotherapy drug doses and to determine the following doses by considering cancer patients’ three weight patterns (increasing, decreasing, and random order). Extensive simulation results and comparative assessment with other current computational chemotherapy drug scheduling models validate the effectiveness and the superiority of the model proposed in this study over the other methods reported in relevant studies.

In Bangladesh, fishing communities are one of the most climate-vulnerable groups, though they play an important role in economic development. The main objective of this study was to identify vulnerability by exploring exposure (i.e., lack of regulating services or household capitals), susceptibility (i.e., lack of access to provisioning services), and lack of resilience (i.e., lack of alternative livelihoods and capacity) and to explore adaptation options, and challenges to understand risk governance. The study considered 45 published research articles for analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Keywords were used in combinations (e.g., fishing communities and Bangladesh) to identify and screen published articles. Articles published in English focusing on vulnerability and/or risk governance, published between 2011 and 2022, featuring original empirical data or a comprehensive systematic review, and published in peer-reviewed journals were included. Articles were excluded if vulnerability and risk governance were evaluated but did not fit or match the definition used in this study. The study found frequent disasters and ocean warming caused different stresses, such as reduced fish catch and income, and resulted in an increased risk of fisheries conflict. Moreover, fishing communities have limited access to properties, modern fishing equipment, financial institutions, and fisher-centered organizations. Adaptation strategies include ecosystem-based (e.g., plantation, payment for ecosystem services) and non-ecosystem-based (e.g., temporary migration, getting help from neighbors) approaches. To boost fish production, the Government of Bangladesh instituted fishing restrictions and social safety net programs (e.g., distributing rice during the fishing restrictions); both initiatives were helpful. However, the conservation policies are not being implemented properly, and there is no particular social welfare, such as banking systems, and low- or no-interest loans being provided that may support fishers to buy fishing equipment or generate alternative income sources. Considering the previous evidence of risks, this paper recommends that fisheries conservation policies be implemented properly, and fishing communities be provided with insurance services and no- or low-interest loans. This will contribute to reducing the climate-induced social-ecological risk and improve sustainable livelihoods that can withstand any regional, national, or local crisis.

The advancement in blockchain applications in the smart sustainable city infrastructure is evaluated in this paper through a comprehensive mapping review. The evaluation is carried out by posing four research questions that address current developments in blockchain technology in the context of smart cities and point out areas where additional study is needed. This study also includes a scoping of blockchain applications in a smart city context to highlight the obstacles to incorporating blockchain technology into smart city infrastructure. Finally, some suggestions for overcoming the problems of incorporating blockchain technology with smart city infrastructure are offered. This research will help researchers and policymakers gain a better understanding of blockchain applications in smart cities.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

This study focuses on the most river-dependent community in a riverbank city, riverside informal settlement dwellers, who need access to ecosystem services to earn livelihoods. Most urban planning strategies do not consider their use and expectations of open and green spaces, and socio-ecological research on this aspect is limited. Thus, in this research, based on face-to-face in-depth interviews with 79 riverside informal settlement dwellers from three informal settlements in Rajshahi, Bangladesh, we qualitatively reflect on their perceptions of the importance of the ecosystem services provided by the river landscape for their everyday lives and livelihoods. The research findings indicate that informal settlementdwellers value rivers for enabling their life by providing food, water, cool breeze, shades from trees, recreation, social connection, mental satisfaction, and aesthetics. Besides, they also acknowledged the economic benefits of the river landscape. In several cases, urban development has led to gentrification by limiting informal settlement dwellers access to riverside open and green spaces. Riverside informal settlement dwellers remain largely excluded in urban ecological planning, even though they are among the groups most impacted by the scarcity of open and green spaces at the riverside. The findings of this study will help urban planners, socio-ecological researchers, and landscape designers to focus on urban river management strategies from the perspective of its bank informal settlement dwellers for inclusive and sustainable urban development.

This paper proposes a fuzzy logic controller (FLC) based variable resistive bridge type fault current limiter (VR-BFCL) to minimize the impact of faults on voltage source converter high voltage direct current (VSC-HVDC) systems. FLC for VR-BFCL is developed to dynamically insert the appropriate current limiting resistance during contingencies to maintain the fault currents within the acceptable limit. The proposed controller effectively provides variable duties to generate and insert controllable resistance in the system based on fluctuation of direct current (DC) link voltage and point of common coupling (PCC) voltage. The efficacy of the proposed controller is justified through simulations work for balanced and unbalanced faults. The results demonstrate that oscillations in PCC voltage and DC link voltage can be maintained within the acceptable range by controlling fault current through the insertion of the appropriate resistance. It is also found that the proposed controller outperforms the existing method.

The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the Saudi Vision 2030 will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

The infection with coronavirus disease (COVID-19) had an extremely negative influence on public health and the global economy. Covid-19 infection is more likely to affect the elderly than younger people, and pre-existing medical conditions, such as cardiovascular disease, diabetes, high blood pressure, and respiratory diseases, might lead to death due to COVID-19 infection. In low-income, developing, and highly dense countries like Bangladesh, the aging population is particularly vulnerable to the pandemic due to inadequate health services, socio-economic circumstances, environmental settings, religious and cultural beliefs, personal cleanliness habits, and a contemplative approach to infectious disease. Besides, recent cyclones and floods have combined effects on older people's increasing vulnerabilities. In this study, we reviewed and examined the vulnerabilities of older adults to the COVID-19 outbreak in Bangladesh. Different mitigation measures are discussed to protect the elderly from the adverse effect of the pandemic. This study proposes several steps to reinforce the commitment to social care and health care services to guarantee well-being, encourage preventive measures, and increase access to older people's health services in Bangladesh. The core findings will provide a valuable guideline for older adults, scientists, and policymakers to take effective long-term measures to mitigate the pandemic's risk.

Heatwave is now becoming an alarming climate-induced shock in Bangladesh. Several investigations mainly focus on the impacts of heatwave and mitigation strategies on megacity and urban heat islands. However, understanding adaptation strategies for livable habitat to the heatwave in a medium-sized city like Rajshahi, located north-western region of Bangladesh, are not well articulated. Therefore, how city dwellers can be adapted to the heatwave in a medium-sized city is crucial to academia, GO's, and NGO's policymakers. The main objective of this paper is to investigate the household adaptation strategies for livable habitat to the heatwave in Rajshahi city corporation (RCC) areas where the overall livelihood of the city dwellers has been severely affected due to heatwave. The findings are based on empirical data collected through field observation, household surveys, and key informant interviews (KII) with a semi-structured questionnaire in RCC. A pre-tested questionnaire was developed to collect primary data from 300 respondents in Rajshahi. Sample sizes were chosen by using the purposive sampling technique. The study found three types of adaptation strategies: 1) structural: designed cooling facilities, uses of air conditioner and cooler; 2) non-structural: drinking plenty of water, staying indoors, taking a bath, sitting beside water bodies; and (3) community innovations based: sitting under big tree and riverside, keeping water reservoir in the room, opening multiple windows, roof and courtyard gardening, wall painting by white color. The community innovation-based adaptation strategies have shown high effectiveness among all types of adaptation strategies for making livable habitats for the heatwave at the household level. Therefore, it can be a significant component of adopting a policy to address the adverse impacts of the heatwave in the medium-sized city of Bangladesh besides structural and non-structural adaptation strategies.

Coronavirus disease 2019 (COVID-19) has become a significant global public health issue resulting from SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). COVID-19 outbreak approaches an unprecedented challenge for human health, the economy, and societies. The transmission of the COVID-19 is influenced by many factors, including climatic, environmental, socioeconomic, and demographic. This study aimed to investigate the influences of climatic and sociodemographic determinants on COVID-19 transmission. The climatic variables considered herein were air temperature, relative humidity, wind speed, air pollution, and cumulative precipitation. Sociodemographic variables included population density, socioeconomic conditions, misinformation, and personal hygiene practices towards the pandemic. Review results indicated that lower temperatures and greater incidence of COVID-19 are reported in a more significant number of studies. Another factor linked to COVID-19 occurrence was the humidity. However, the results were varied; some research reported positive, and others reported negative relationships. In addition, poor air quality, along with strong winds, makes the virus more vulnerable to spreading, leading to a spike in COVID-19 cases. PM2.5, O3, and NO2 also showed a strong correlation with the recent epidemic. The findings on rainfall were inconsistent between studies. Among the non-climatic factors, population density, education, and income were credited as potential determinants for the coronavirus outbreak. Climatic and sociodemographic factors showed a significant correlation on the COVID-19 outbreak. Thus, our review emphasizes the critical importance of considering climatic and non-climatic factors while developing intervention measures. This study's core findings will support the decision-makers in identifying climatic and socioeconomic elements that influence the risks of future pandemics.

A paradigm shift in power systems is observed due to the massive integration of renewable energy sources (RESs) as distributed generators. Mainly, solar photovoltaic (PV) panels and wind generators are extensively integrated with the modern power system to facilitate green efforts in the electrical energy sector. However, integrating these RESs destabilizes the frequency of the modern power system. Hitherto, the frequency control has not drawn sufficient attention due to the reduced inertia and complex control of power electronic converters associated with renewable energy conversion systems. Thus, this article provides a critical summary on the frequency control of solar PV and wind-integrated systems. The frequency control issues with advanced techniques, including inertia emulation, de-loading, and grid-forming, are summarized. Moreover, several cutting-edge devices in frequency control are outlined. The advantages and disadvantages of different approaches to control the frequency of high-level RESs integrated systems are well documented. The possible improvements of existing approaches are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians.

The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

An autonomous microgrid is often formed by incorporating distributed generators into the distribution system. However, distributed generators have less inertia compared to traditional synchronous generators, and can cause the system frequency to become unstable. Additionally, as more clusters are integrated into the distribution microgrid, frequency instability increases. To resolve frequency instability in the microgrid cluster, this study proposes a supercapacitor control approach. The microgrid consists of several clusters which integrate wind power generators, solar PV, STP, fuel cells, aqua electrolyzers, and diesel generators. Initially, a small signal model is developed to facilitate the control design. A fractional-order supercapacitor controller is augmented with the developed small-signal model to stabilize the frequency of the microgrid. Furthermore, the controller parameters are optimized to guarantee robust controller performance. The proposed fractional-order supercapacitor controller provides more degrees of freedom compared to the conventional controller. Time-domain simulations were carried out considering several real-time scenarios to test the performance of the proposed controller. We observed that the presented approach is capable of stabilizing the system frequency in all cases. Furthermore, the proposed approach outperforms existing approaches in stabilizing the frequency of the microgrid cluster.

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.