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| 1 | Effect of Peer Group Activity - Based Learning on Student's Aca- demic Achievement in Physics at Secondary Level显示文摘 | Safqat Hussain Saeed Anwar and Muhammad Iqbal Majoka | 2011 | Internation- al Journal of Academic Research2011，3，1： | 1 |
| 2 | Understanding the Language of ISIS:An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning显示文摘The internet,particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals.Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization.Consequently,social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare.Thus,recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle.The aim of this research work is to identify radical text in social media.Our contributions are as follows:(i)A new dataset to be employed in radicalization detection;(ii)In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified;(iii)An approach to train classifier employing religious features along with radical features to detect radicalization;(iv)Observing the use of violent and bad words in radical,neutral and random groups by employing violent,terrorism and bad words dictionaries.Our research results clearly indicate that incorporating religious text in model training improves the accuracy,precision,recall,and F1-score of the classifiers.Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance.In addition to this,violence and bad words are creating a differentiating factor between radical and random users but for neutral(anti-ISIS)group it needs further investigation. | Zia Ul Rehman Sagheer Abbas Muhammad Adnan Khan Ghulam Mustafa Hira Fayyaz Muhammad Hanif Muhammad Anwar Saeed | 2021 | Computers, Materials & Continua2021，，2： | 0 |
| 3 | NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs显示文摘Delay Tolerant Networks(DTNs)have the major problem of message delay in the network due to a lack of endto-end connectivity between the nodes,especially when the nodes are mobile.The nodes in DTNs have limited buffer storage for storing delayed messages.This instantaneous sharing of data creates a low buffer/shortage problem.Consequently,buffer congestion would occur and there would be no more space available in the buffer for the upcoming messages.To address this problem a buffer management policy is proposed named“A Novel and Proficient Buffer Management Technique(NPBMT)for the Internet of Vehicle-Based DTNs”.NPBMT combines appropriate-size messages with the lowest Time-to-Live(TTL)and then drops a combination of the appropriate messages to accommodate the newly arrived messages.To evaluate the performance of the proposed technique comparison is done with Drop Oldest(DOL),Size Aware Drop(SAD),and Drop Larges(DLA).The proposed technique is implemented in the Opportunistic Network Environment(ONE)simulator.The shortest path mapbased movement model has been used as the movement path model for the nodes with the epidemic routing protocol.From the simulation results,a significant change has been observed in the delivery probability as the proposed policy delivered 380 messages,DOL delivered 186 messages,SAD delivered 190 messages,and DLA delivered only 95 messages.A significant decrease has been observed in the overhead ratio,as the SAD overhead ratio is 324.37,DLA overhead ratio is 266.74,and DOL and NPBMT overhead ratios are 141.89 and 52.85,respectively,which reveals a significant reduction of overhead ratio in NPBMT as compared to existing policies.The network latency average of DOL is 7785.5,DLA is 5898.42,and SAD is 5789.43 whereas the NPBMT latency average is 3909.4.This reveals that the proposed policy keeps the messages for a short time in the network,which reduces the overhead ratio. | Sikandar Khan Khalid Saeed Muhammad Faran Majeed Salman A.AlQahtani Khursheed Aurangzeb Muhammad Shahid Anwar | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
| 4 | Estimation of soil carbon pools in the forests of Khyber Pakhtunkhwa Province, Pakistan显示文摘Forest soils have high carbon densities compared to other land-uses.Soil carbon sequestration is important to reduce CO 2 concentrations in the atmosphere.An eff ective climate change mitigation strategy involves limiting the emissions of greenhouse gases from soils.Khyber Pakhtunkhwa is the most forested province of Pakistan,hosting about one-third of the country’s 4.5×106 ha forest area.Soil organic carbon in the province’s forests was estimated through a fi eld-based study carried out during 2014–17 covering the whole province.Data was collected from 373 sample plots laid out in diff erent forest types using a stratifi ed cluster sampling technique.The total quantity of soil organic carbon was estimated at 59.4×106 t with an average of 52.4±5.3 t/ha.About 69%of the total soil carbon is present in temperate forests.Subtropical broad-leaved and subtropical pine forests constitute 11.4%and 8.8%of the soil carbon stock respectively.Similarly,subalpine and oak forests have respective shares of 5.1%and 5.7%in the soil carbon pool.The lowest carbon stock(0.1%)was found in dry-tropical thorn forests.The highest soil carbon density was found in subalpine forests(69.5±7.2 t/ha)followed by moist temperate forests(68.5±6.7 t/ha)and dry temperate forests(60.7±6.5 t/ha).Oak forests have carbon density of 43.4±7.1 t/ha.Subtropical pine,subtropical broad-leaved and dry tropical thorn forests have soil carbon densities of 36.3±3.7,32.8±6.2 and 31.5±3.5 t/ha,respectively.The forests of the Khyber Pakhtunkhwa province have substantial amounts of soil carbon which must be conserved for climate change mitigation and maintenance of sound forest health. | Anwar Ali Muhammad Irfan Ashraf Saeed Gulzar Muhammad Akmal Bilal Ahmad | 2020 | Journal of Forestry Research2020，31，6： | 0 |
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